502 20 16481 https://archives.christuniversity.in/files/original/6cceedb5891e3153f618ea1c1386aeed.png ae8597b1fb62da5294ca14f6fc0ece85 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learning based EEG signal processing for smart patient monitoring system / Subject The topic of the resource Bio-Medical Engineering Description An account of the resource Patent Number: 202141022214, Applicant: Dr. Ganesh Kumar R. In the current pandemic situation, patients with critical diseases are lacking immediate care which would reduce the mortality rate. This invention focuses on continuous monitoring of patient™s EEG signals for occurrence of any seizures in brain signals. This system is designed using machine learning algorithm for resource optimization thereby implemented using VLSI technology. Creator An entity primarily responsible for making the resource R, Ganesh Kumar. Source A related resource from which the described resource is derived <a href="https://drive.google.com/file/d/1AGSh4iRheSHx2iXeRqCZzqq_biaOlRBb/view" target="_blank" title="Machine learning based EEG signal processing for smart patient monitoring system /" rel="noreferrer noopener">https://drive.google.com/file/d/1AGSh4iRheSHx2iXeRqCZzqq_biaOlRBb/view</a> Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2021-03-29 Language A language of the resource English Type The nature or genre of the resource Patent Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning based Food Sales Prediction using Random Forest Regression Subject The topic of the resource Artificial Intelligence; Food; Forecasting; Machine Learning; Regression; Sales prediction Description An account of the resource Sales forecasting is crucial in the food industry, which experiences high levels of food sales and demand. The industry has concentrated on a well-known and established statistical model. Due to modern technologies, it has gained tremendous appeal in improving market operations and productivity. The main objective is to find the most accurate algorithms to predict food sales and which algorithm is most suitable for sales forecasting. This research work has mentioned and discussed about several research articles that revolve around the techniques usedfor sales prediction as well as finding out the advantages and disadvantages of the said techniques. Various techniques were discussed as to predicting the sales but mainly Incline Increasing Regression and Accidental Forestry Lapse is used for attention. The manufacturing has concentrated on a well-known and established statistical model. Although algorithms like Modest Direct Regression, Incline Increasing Lapse, Provision Course Lapse, Accidental Forest Lapse, Gradient Boosting Regression, and Random Forest Regression are well familiar for outdoing others, it has remained decisively established that Random Forest Regression is the most appropriate technique when associated to the others. After doing the whole examination, the Random Forest Regression technique fared well when compared to other algorithms. The feature importance is generated for the selected dataset using Python and Random Forest Regression and the nose position chart is also explainedin detail. The proposed model is compared three major parameters that are accuracy score, mean absolute error and max error. The proposed random forest regression accuracy score is improved nearly 1.83% and absolute error rate is reduced 4.66%. 2022 IEEE. Creator An entity primarily responsible for making the resource Naik H.; Yashwanth K.; Suraj P.; Jayapandian N. Source A related resource from which the described resource is derived 6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022 - Proceedings, pp. 998-1004. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA55336.2022.10009277" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA55336.2022.10009277</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147508339&amp;doi=10.1109%2FICECA55336.2022.10009277&amp;partnerID=40&amp;md5=1edc820e13d33dfa59ab3cf1f4e4b76a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147508339&amp;doi=10.1109%2fICECA55336.2022.10009277&amp;partnerID=40&amp;md5=1edc820e13d33dfa59ab3cf1f4e4b76a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166548271-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Naik H., Christ University, Department of Cse, India; Yashwanth K., Christ University, Department of Cse, India; Suraj P., Christ University, Department of Cse, India; Jayapandian N., Christ University, Department of Cse, India https://archives.christuniversity.in/files/original/1e6c49bfd80a208996446f29f3f19435.jpg 96e0cb74b7edc75db27cde5309115286 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learning based intelligent sensor framework to assist farmers in weather forecasting for appropriate crop cultivation / Subject The topic of the resource Mechanical Engineering Description An account of the resource Patent Number: 202141047024, Applicant: Dr.S.Balamurugan. Weather forecasting is an important factor in agricultural sector that aid farmers for sowing and reaping appropriate crops. The day-to-day weather forecast aid farmers to decide upon the type of irrigation, time of yield, choice of the crop to be cultivated that ultimately leads to profit/loss business decision in agriculture. For profitable and successful farming and harvesting the farmer has to be aware of several factors affecting the agriculture such as temperature, humidity, UV radiation, wind direction, solar radiation, barometric pressure and rainfall. Proposed is a machine learning based intelligent sensor framework to forecast weather for appropriate crop cultivation. A set of sensors that are deployed at a focused operating distance in the farm is capable to provide weather analytics report to farmers. The analytics is performed using machine learning algorithms for data processing. Creator An entity primarily responsible for making the resource N, Arul Kumar. Source A related resource from which the described resource is derived <a href="https://drive.google.com/file/d/1QG8-HfVNnCp1vbn5xwP57J3qRezaQKAf/view" target="_blank" title="Machine learning based intelligent sensor framework to assist farmers in weather forecasting for appropriate crop cultivation /" rel="noreferrer noopener">https://drive.google.com/file/d/1QG8-HfVNnCp1vbn5xwP57J3qRezaQKAf/view</a> Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2021-05-06 Language A language of the resource English Type The nature or genre of the resource Patent Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning based Loan Eligibility Prediction using Random Forest Model Subject The topic of the resource Borrowers; Customer Prediction; Data Collecction; Lloan Eligibility; Machine Learning; Random Forest Description An account of the resource When one or more people, organizations, or other entities lend money to other people, organizations, or entities, it is known as a loan. The recipient (that is the borrower) takes on a debt for which he or she is normally accountable for interest payments until the loan is repaid. The major goal of this proposed model is to ensure that an individual, institution, or organization seeking for a loan is properly verified before granting them the loan they require. Before authorizing a loan for any individual or business several factors must be considered. That including gender, education, and the number of dependents. The goal of proposed model is to automate the method, which will save time and energy while improving the efficiency of the process. This particular process input is having two different kind of data set. First one is train data set and second set is test data set. The first date set that is train data set is generally used to train and assess the machine learning model accuracy. The loan eligibility predictions are generated using the test data set. To forecast loan eligibility and train this random forest, machine learning method called Random Forest. The proposed random forest model is providing higher accuracy level. This model is providing 28 % higher accuracy level compare to regular prediction. 2022 IEEE. Creator An entity primarily responsible for making the resource Reddy C.S.; Siddiq A.S.; Jayapandian N. Source A related resource from which the described resource is derived 7th International Conference on Communication and Electronics Systems, ICCES 2022 - Proceedings, pp. 1073-1079. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCES54183.2022.9835875" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCES54183.2022.9835875</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136323011&amp;doi=10.1109%2FICCES54183.2022.9835875&amp;partnerID=40&amp;md5=7fcf8f6c66506dc06bba7a8b81831425" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136323011&amp;doi=10.1109%2fICCES54183.2022.9835875&amp;partnerID=40&amp;md5=7fcf8f6c66506dc06bba7a8b81831425</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549634-6 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Reddy C.S., Christ, Deemed to Be University, Department of Cse, Bangalore, India; Siddiq A.S., Christ, Deemed to Be University, Department of Cse, Bangalore, India; Jayapandian N., Christ, Deemed to Be University, Department of Cse, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization Subject The topic of the resource Binary coati optimisation algorithm (BCOA); Chronic kidney disease (CKD); Feature selection (FS); Optimisation algorithm; Texture Description An account of the resource Chronic kidney disease (CKD) one of the most dangerous illnesses. Early detection is vital for improving survival rates and underscoring the need for an intelligent classifier to differentiate between normal and abnormal kidney ultrasound images. Features extracted from an image have a significant impact on classification accuracy. In this study, we present a Binary Coati optimization algorithm (BCOA) for feature selection in CKD, which focuses on reducing the high dimensionality features extracted from ultrasound images, including GLCM, GLRLM, GLSZM, GLDM, NGTDM, and first order, by employing BCOA-S shaped and BCOA-V shaped transfer functions that convert BCOA from a continuous search space to a binary form, which helps in the selection of optimal features to improve the classification performance while reducing the feature dimensionality. The reduced feature was evaluated using six machine-learning classifiers: Random Forest, Support Vector Machine, Decision tree, K-nearest Neighbor, XG-boost, and Nae Bayes. The efficiency of the proposed framework was assessed based on accuracy, precision, recall, specificity, f1 score and AUC curve. BCOA-V outperformed in terms of accuracy, precision, recall, specificity, F1 score and AUC curve by 99%,100%,97%,100%, 98%, and 98%, respectively. This makes it a superior choice for CKD diagnosis and is a valuable tool for feature selection in medical diagnosis. (2024), (Intelligent Network and Systems Society). All rights reserved. Creator An entity primarily responsible for making the resource Kausar F.; Ramamurthy B. Source A related resource from which the described resource is derived International Journal of Intelligent Engineering and Systems, Vol-17, No. 6, pp. 1300-1313. Publisher An entity responsible for making the resource available Intelligent Network and Systems Society Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22266/ijies2024.1231.94" target="_blank" rel="noreferrer noopener">https://doi.org/10.22266/ijies2024.1231.94</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208113146&amp;doi=10.22266%2Fijies2024.1231.94&amp;partnerID=40&amp;md5=fe7d1e209b17077c8524f1e946ee92f0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208113146&amp;doi=10.22266%2fijies2024.1231.94&amp;partnerID=40&amp;md5=fe7d1e209b17077c8524f1e946ee92f0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2185310X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kausar F., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India; Ramamurthy B., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Natarajan, Jayapandian Title A name given to the resource Machine Learning Based Parking Space Classification Using R-CNN and Faster R-CNN FPN Architecture Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1636 LNNS;pp.64-73 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-05802-7_8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-05802-7_8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020165029?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020165029?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Natarajan J., School of Engineering and Technology, Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India Description An account of the resource This research work aims to create an accurate and economical model for classifying parking space using deep learning techniques. Using current advances in deep learning and computer vision, the proposed model solves urban mobility difficulties, particularly parking management. To address parking space occupancy classification, the research work suggests using two proven deep learning architectures, R-CNN (Region-based Convolutional Neural Networks) and Faster R-CNN FPN (Feature Pyramid Network), as well as insights from previous research. The proposed models take advantage of the R-CNN and Faster R-CNN FPN architectures. This solution uses binary classifiers, such as ResNet50, to assess image patches representing individual parking spaces and offer precise occupancy values. Furthermore, this research investigates the Faster R-CNN FPN architecture, which uses a feature pyramid network to record hierarchical information and reason about complex spatial configurations in parking lots. The proposed models stand out for their ability to use high-resolution photos from real-world parking lots, allowing them to learn discriminative features automatically from raw image data. This differs from traditional methods that rely on handcrafted features, allowing the models to manage a wide range of parking lot circumstances, including changes in weather, illumination, and occlusions caused by surrounding vehicles or barriers. This research intends demonstrate the improved performance and scalability of deep learning models for parking space occupancy classification by conducting extensive testing. Here the implementation method focuses on systematic data collection, annotation, preprocessing, and model training to create machine learning models that can reliably categorize parking spot occupancy, allowing for successful parking management solutions in real-world scenarios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Artificial Intelligence; Convolutional Neural Networks; Deep learning; Feature Pyramid Network; Parking Space Classification Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303205801-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning based Plant Disease Identification by using Hybrid Nae Bayes with Decision Tree Algorithm Subject The topic of the resource Artificial Intelligence; Convoluted Neural Networks; Decision Tree; Machine Learning; Nae Bayes Description An account of the resource Artificial intelligence or machine learning as a domain started as a distinct domain marketplace for enthusiasts. Over an extended period of time, this has evolved into an industry with boundless potential. This is the focal point of a plethora of technologies like real-time analytics, deep learning in computer science. It's inherent to various customer needs such as fault detection, home automation, health monitoring devices as well as appliances, and multiple RPM devices Artificial intelligence which has been tested and trained to recognize and determine a plethora of flaws and inaccuracies. This could be intriguing procedures in day-to-day applications. An unimaginable number of prediction models, packages, libraries as well as sensors are utilized to sieve through flaws with the aid of mobile app development and other multispectral sensors. These trendy devices have become ever present and a part of our extensive routine. The demand for dependable and efficient algorithms is satisfied while implementing these devices. The objective primarily dictates emphasis on the prediction of plant diseases in the agricultural arena in reality by providing aid in the field of agriculture, and industry. In this case, the device incorporates a database which stores and keeps track of previously detected flaws or defects. In addition, the history of detected plant infections is maintained in an online repository. This can help with the forecast of the defects within the gadgets that are to be enhanced. Furthermore, the suggested approach of this text inculcates the invigilation of every leaf in the plant via machine learning model. Hence, this approach of implementation limits interaction of humans with the interface and it detects disease ridden plants efficiently with accuracy. The plant disease identification problem is to solve the proposed hybrid Nae Bayes with Decision Tree algorithm. The proposed model provides higher accuracy level compare to the regular model. 2023 IEEE. Creator An entity primarily responsible for making the resource Yetukuri N.K.; Maddali K.; Jayapandian N. Source A related resource from which the described resource is derived Proceedings - 5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023, pp. 852-858. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSSIT55814.2023.10061099" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSSIT55814.2023.10061099</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150679515&amp;doi=10.1109%2FICSSIT55814.2023.10061099&amp;partnerID=40&amp;md5=f6b8941020a61c77270cd0de26c16b75" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150679515&amp;doi=10.1109%2fICSSIT55814.2023.10061099&amp;partnerID=40&amp;md5=f6b8941020a61c77270cd0de26c16b75</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166547467-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Yetukuri N.K., Christ (Deemed to Be University), Department of Cse, India; Maddali K., Christ (Deemed to Be University), Department of Cse, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, India https://archives.christuniversity.in/files/original/f888e8de7aae88274724ccd3a39b5da6.jpg 0b6dfeeadbd8dbf97f5aef738454f6b9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learning based price negotiating e-commerce chatbot system / Subject The topic of the resource Computer Science Description An account of the resource Patent Number: 202141050967, Applicant: Dr .V. Vijayagopal. Machine learning based Price negotiator E-commerce chatbot system Abstract: On the e-commerce website described in this study, artificial intelligence chat bots are used. This chatbot can help to simplify the price negotiation process on an e-commerce website. The chatbot communicates with the user using user-friendly language. This chatbot has been integrated into an online market website. Creator An entity primarily responsible for making the resource T, Ravikumar. Source A related resource from which the described resource is derived <a href="https://drive.google.com/file/d/1uBRPZfKw9KeKxV0kHdNNdc0K02KFlnQ5/view" target="_blank" title="Machine learning based price negotiating e-commerce chatbot system /" rel="noreferrer noopener">https://drive.google.com/file/d/1uBRPZfKw9KeKxV0kHdNNdc0K02KFlnQ5/view</a> Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2021-05-06 Language A language of the resource English Type The nature or genre of the resource Patent Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Based Recession Prediction Analysis Using Gross Domestic Product (GDP) Subject The topic of the resource Covid-19; Economical Statistics; Gross Domestic Product; Machine Learning; Random Forest; Recession Description An account of the resource This research article aims to explore the prediction and analysis of recessions, with a particular focus on Gross Domestic Product (GDP). The study examines the impact of recessions on different countries, namely India, USA, Germany, China, and Bangladesh, while also considering the influence of the COVID-19 pandemic on these nations in relation to the recessionary effects. Furthermore, the study lists many machine learning techniques that could be used to anticipate recessions. This research mainly focuses on predicting recession using different machine learning models. The research not only provides an in-depth analysis of the recessionary impacts on different economies but also serves as a foundation for future implementation of these algorithms for accurate recession prediction and proactive economic decision-making. This research study mainly focuses on machine learning algorithms like Random Forest, Support Vector Machines and Regression Model. The GDP prediction comparison is taking last twenty years data. This is mainly compared before and after COVID-19 situation. 2023 IEEE. Creator An entity primarily responsible for making the resource Haritha T.H.; Joby A.; Javapandian N. Source A related resource from which the described resource is derived 7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 492-498. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICECA58529.2023.10394958" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICECA58529.2023.10394958</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186127859&amp;doi=10.1109%2FICECA58529.2023.10394958&amp;partnerID=40&amp;md5=95694b94d6b3e6f157bce4efb01c1581" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186127859&amp;doi=10.1109%2fICECA58529.2023.10394958&amp;partnerID=40&amp;md5=95694b94d6b3e6f157bce4efb01c1581</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034060-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Haritha T.H., CHRIST (Deemed to Be University), Department of CSE, India; Joby A., CHRIST (Deemed to Be University), Department of CSE, India; Javapandian N., CHRIST (Deemed to Be University), Department of CSE, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Based recommendation system Using User-Item Interaction Subject The topic of the resource Clustering; Collaborative Filtering; Content-based Filtering; Machine Learning; Movie Recommendation; User ratings Description An account of the resource Electronic commerce, or e-commerce, is the activity of trading services and commodities through the internet. Identifying the item that the consumer may buy from the enormous number of possibilities accessible to solve this difficulty is now one of the key difficulties encountered by most E-commerce businesses. Recommender systems have been implemented. Recommender systems (RS) are systems that collect information from users about their preferences and allow them to make decisions from the available options. Today, various recommender systems are growing with the advent of web-based information. As you can see from various articles, such recommender systems are used in a variety of industries, from simple objects to more sophisticated objects.RS has gained popularity in the previous decade, particularly in the realm of E-Commerce and related sectors. This report aims to identify recent developments as well as their potential for improvement. It is intended to elaborate on a number of points. And also work more on user-item based recommendation. These types of user-item based recommendation will be more effective in fashion area. 2022 IEEE. Creator An entity primarily responsible for making the resource Akshay K.; Nair A.M. Source A related resource from which the described resource is derived 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022, pp. 1745-1749. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACITE53722.2022.9823576" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACITE53722.2022.9823576</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135477888&amp;doi=10.1109%2FICACITE53722.2022.9823576&amp;partnerID=40&amp;md5=d1f393443f01abc86211f1c8c79c7ca0" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135477888&amp;doi=10.1109%2fICACITE53722.2022.9823576&amp;partnerID=40&amp;md5=d1f393443f01abc86211f1c8c79c7ca0</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166543789-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Akshay K., Christ (Deemed to Be University), Lavasa Campus, Department of Data Science, Maharashtra, Pune, India; Nair A.M., Christ (Deemed to Be University), Lavasa Campus, Department of Data Science, Maharashtra, Pune, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Based Spam E-Mail Detection Using Logistic Regression Algorithm Subject The topic of the resource E-Mail; Logistic Regression; Machine Learning; Nae Bayes; Spam detection; Support Vector Machines Description An account of the resource The rise of spam mail, or junk mail, has emerged as a significant nuisance in the modern digital landscape. This surge not only inundates user's email inboxes but also exposes them to security threats, including malicious content and phishing attempts. To tackle this escalating problem, the proposed machine learning-based strategy that employs Logistic Regression for accurate spam mail prediction. This research is creating an effective and precise spam classification model that effectively discerns between legitimate and spam emails. To achieve this, we harness a meticulously labeled dataset of emails, each classified as either spam or non-spam. This model is to apply preprocessing techniques to extract pertinent features from the email content, encompassing word frequencies, email header data, and other pertinent textual attributes. The choice of Logistic Regression as the foundational classification algorithm is rooted in its simplicity, ease of interpretation, and appropriateness for binary classification tasks. To process train the model using the annotated dataset, refining its hyper parameters to optimize its performance. By incorporating feature engineering and dimensionality reduction methodologies, bolster the model's capacity to generalize effectively to unseen data. Our evaluation methodology encompasses rigorous experiments and comprehensive performance contrasts with other well-regarded machine learning algorithms tailored for spam classification. The assessment criteria encompass accuracy, precision, recall, and the F1 score, offering a holistic appraisal of the model's efficacy. Furthermore, we scrutinize the model's resilience against diverse forms of spam emails, in addition to its capacity to generalize to new data instances. This model is to findings conclusively demonstrated that our Logistic Regression-driven spam mail prediction model achieves a competitive performance standing when juxtaposed with cutting-edge methodologies. The model adeptly identifies and sieves out spam emails, thereby cultivating a more trustworthy and secure email environment for users. The interpretability of the model lends valuable insights into the pivotal features contributing to spam detection, thereby aiding in the identification of emerging spam patterns. 2023 IEEE. Creator An entity primarily responsible for making the resource Livia Shreenithi S.A.; Yougandar S.V.; Jayapandian N. Source A related resource from which the described resource is derived 3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG59752.2023.10455970" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG59752.2023.10455970</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189241095&amp;doi=10.1109%2FICTBIG59752.2023.10455970&amp;partnerID=40&amp;md5=872eacfb907b5a86092fd0818d138365" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189241095&amp;doi=10.1109%2fICTBIG59752.2023.10455970&amp;partnerID=40&amp;md5=872eacfb907b5a86092fd0818d138365</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835034327-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Livia Shreenithi S.A., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Yougandar S.V., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Jayapandian N., Christ (Deemed to Be University), Department of Cse, Bangalore, India https://archives.christuniversity.in/files/original/4befac0c7786ecb2d262d1e783910b01.png ae8597b1fb62da5294ca14f6fc0ece85 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Patents Subject The topic of the resource Patent Description An account of the resource Patent Patent Patents Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learning based system for evaluation and prediction of lung cancer using computed tomography images / Subject The topic of the resource Computer Science Description An account of the resource Patent Number: 202011038445, Applicant: Puneet Kumar Aggarwal. The present invention relates to a machine learning based system for evaluation and prediction of lung cancer using computed tomography images. The objective of the present invention is to solve the problems in the prior art related to adequacies in technologies of diagnosis and prediction of the lung cancer. Creator An entity primarily responsible for making the resource Prathap, Boppuru Rudra. Source A related resource from which the described resource is derived <a href="https://drive.google.com/file/d/16uhwicNbFbRfNsySGyedmvP6jqpqFhvw/view" target="_blank" title="Machine learning based system for evaluation and prediction of lung cancer using computed tomography images /" rel="noreferrer noopener">https://drive.google.com/file/d/16uhwicNbFbRfNsySGyedmvP6jqpqFhvw/view</a> Publisher An entity responsible for making the resource available Intellectual Property India Date A point or period of time associated with an event in the lifecycle of the resource 2020-03-18 Language A language of the resource English Type The nature or genre of the resource Patent Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Based Time Series Analysis for COVID-19 Cases in India Subject The topic of the resource Covid-19; forecasting; India; Machine learning; Prediction model; seasonality; Time series analysis; Trends Description An account of the resource The World Health Organization declared the Coronavirus Infection, or COVID-19, to be widespread. One of the most appropriate methodologies for COVID-19 is time series analysis. The most appropriate technique for COVID-19 is time series analysis. It can be applied to Recognizing Information Patterns and Predicting Insights. The paper summarises the components of time series using the COVID-19 dataset for India as an example of one of the most important methodologies in predictive analytics. Time series models are chosen because they can predict future outcomes, comprehend prior outcomes, provide strategy recommendations, and much more. These common goalrists of temporal arrangement modelling do not differ significantly from those of cross-sectional or board data modelling. Machine Learning may be a well-known fact that it is an excellent technique for imagining, discourse, and standard dialect management for a large clarified accessible dataset. The results for confirmed, recovered, and death cases are presented in this study. 2022 IEEE. Creator An entity primarily responsible for making the resource Sagaya Aurelia P.; Aruna S K.; Rekha V. Source A related resource from which the described resource is derived 8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022, pp. 1932-1937. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACCS54159.2022.9785100" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACCS54159.2022.9785100</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133212432&amp;doi=10.1109%2FICACCS54159.2022.9785100&amp;partnerID=40&amp;md5=a9c194fa68f601531d63b5b543c59c9a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133212432&amp;doi=10.1109%2fICACCS54159.2022.9785100&amp;partnerID=40&amp;md5=a9c194fa68f601531d63b5b543c59c9a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166540816-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sagaya Aurelia P., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Aruna S K., Christ (Deemed to Be University), Department of Cse, Karnataka, Bangalore, India; Rekha V., Christ (Deemed to Be University), Department of Cse, Karnataka, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learning based Unique Perfume Flavour Creation Using Quantitative Structure-Activity Relationship (QSAR) Subject The topic of the resource Artificial Intelligence; Data Analytics; Human Intelligence; Machine Learning; Natural Language Processing; Perfume industry Description An account of the resource Artificial intelligence played a vital role in brings revolutionary changes in the field of perfumery. It is much evident with events including the success of Philyra, exhibitions showcasing the ideas of this concept. Machine learning made it user friendly and more comfortable for the users by means of suggestive interaction. Machine learning also benefited the perfumers in helping them to choose the best combinations and likely successful outcomes. With growing concern about a healthy lifestyle, the thoughts about having an artificial intelligence to predict the user friendliness could be a huge success. This definitely would require a huge database comprising a large detail about diseases and the causes and combinational results of the various chemicals used in perfumery. This system may not be a completely successful one but would be reliable to a better extent. It would gain a positive response from various governmental health departments and would be encouraged by the consumers. Also, another possible development would be Artificial intelligence that is able to predict how long a perfume can last. This would let the consumer choose the one that suits the need. Through this idea we could now get a clear idea about the progress that we have made till this day. Further we can also be driven into vague ideas about how the future of Artificial intelligence would likely grow into. Machine learning and deep learning is a major pillar of artificial intelligence with larger application. Coming to our domain of discussion, artificial intelligence changed the way that things were in the past centuries about fragrance. This article proposed Quantitative structure-activity relationship (QSAR) method is used to predict the best perfume flavour. The proposed system also reduces mean absolute error (MAE). The proposed QSAR is also reducing the chemical composition and increase the perfume quality. 2021 IEEE. Creator An entity primarily responsible for making the resource Sushma S.; Sundaram N.; Jayapandian N. Source A related resource from which the described resource is derived Proceedings - 5th International Conference on Computing Methodologies and Communication, ICCMC 2021, pp. 1397-1402. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCMC51019.2021.9418246" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCMC51019.2021.9418246</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106026326&amp;doi=10.1109%2FICCMC51019.2021.9418246&amp;partnerID=40&amp;md5=bcb6064875e39ebe0b11ebafb948ea38" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106026326&amp;doi=10.1109%2fICCMC51019.2021.9418246&amp;partnerID=40&amp;md5=bcb6064875e39ebe0b11ebafb948ea38</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166540360-3 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sushma S., Deemed to Be University, Christ, Department of CSE, Bangalore, India; Sundaram N., University of Applied Sciences Wzburg-Schweinfurt, Faculty of Mechatronics, Germany; Jayapandian N., Deemed to Be University, Christ, Department of CSE, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Classifiers for Credit Risk Analysis Subject The topic of the resource Credit risk; Decision tree; K-Nearest Neighbour; Machine learning; Naive Bayes classifier Description An account of the resource The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis. 2024 Sudiksha et al., licensed to EAI. Creator An entity primarily responsible for making the resource Sudiksha; Nanjundan P.; George J.P. Source A related resource from which the described resource is derived EAI Endorsed Transactions on Internet of Things, Vol-10 Publisher An entity responsible for making the resource available European Alliance for Innovation Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4108/eetiot.5376" target="_blank" rel="noreferrer noopener">https://doi.org/10.4108/eetiot.5376</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187884959&amp;doi=10.4108%2Feetiot.5376&amp;partnerID=40&amp;md5=b342327412a0caadb68881e3df17b106" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187884959&amp;doi=10.4108%2feetiot.5376&amp;partnerID=40&amp;md5=b342327412a0caadb68881e3df17b106</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 24141399 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sudiksha, Department of Data Science, Christ University, Pune Lavasa Campus, India; Nanjundan P., Department of Data Science, Christ University, Pune Lavasa Campus, India; George J.P., Department of Data Science, Christ University, Pune Lavasa Campus, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning Enabled Financial Statements in Assessing a Business's Performance Subject The topic of the resource Business Performance; Ethical Considerations; Financial Analysis; Financial Ratios; Financial Statements; Interpretability; Investor Confidence; Machine Learning; Predictive Analytics; Return on Assets (ROA) Description An account of the resource Machine Learning Enabled Financial Statements (MLEFS) revolutionize corporate performance analysis. This study examines MLEFS's dramatic effects using data gathering, model creation, interpretability, deployment, and ethics. We found that MLEFS accurately predicts crucial financial measures, helping investors, lenders, and financial analysts make better judgments. The study emphasizes the importance of financial measures like Return on Assets (ROA) in supporting financial theories and models. The research also stresses interpretability and ethics, promoting responsible machine learning in finance. Future trends include enhanced interpretability, strong ethical frameworks, real-time analysis, big data integration, regulatory adaption, and industrial acceptance. This study opens the door to data-driven financial analysis and decision-making, improving strategic planning, risk reduction, and investor trust. 2024 IEEE. Creator An entity primarily responsible for making the resource Priya P.S.; Shah J.A.; Aarawal A.; Kalra R.; Kadam S.; Sontakke K.A. Source A related resource from which the described resource is derived Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICONSTEM60960.2024.10568659" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICONSTEM60960.2024.10568659</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842164&amp;doi=10.1109%2FICONSTEM60960.2024.10568659&amp;partnerID=40&amp;md5=4dff6c8297c695b78c840478be3d047e" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198842164&amp;doi=10.1109%2fICONSTEM60960.2024.10568659&amp;partnerID=40&amp;md5=4dff6c8297c695b78c840478be3d047e</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036509-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Priya P.S., Sri Krishna College of Technology, Tamil Nadu, Coimbatore, India; Shah J.A., Amity Business School, Amity University, Maharashtra, Mumbai, India; Aarawal A., Management, Ndiit, Delhi, New Delhi, India; Kalra R., School of Business And Management, CHRIST(Deemed To Be University), Karnataka, Bangalore, India; Kadam S., Symbiosis Institute of Business Management, Pune, Symbiosis International, Deemed University, Maharashtra, Pune, India; Sontakke K.A., Sies College of Management Studies, Department of Finance, Maharashtra, Navi Mumbai, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Padmalal, S.; Arumugam, P.; Baby Anusha, M.; Bamane, Kalyan Devappa; Yamsani, Nagendar; Muppavaram, Kireet Title A name given to the resource Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Machine Learning in Healthcare: Data-Driven Decisions, Predictive Modelling, Personalized Medicine;pp.1771-192 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1515/9783111541235-009" target="_blank" rel="noreferrer noopener">https://doi.org/10.1515/9783111541235-009</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028340536?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028340536?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Padmalal S., Department of Computer Science and Engineering, Mangalam College of Engineering, Ettumanoor, India; Arumugam P., Department of statistics, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, Abishekapatti, 627012, India; Baby Anusha M., Department Of CSE, Lakireddy Bali Reddy College of Engineering, Krishna, Andhra Pradesh, Mylavaram, 521230, India; Bamane K.D., Department of Computer Engineering, D.Y. Patil College of Engineering, Pune, Akurdi, India; Yamsani N., School of Computer Science and Artificial Intelligence, SR University, Telangana, Warangal, 506371, India; Muppavaram K., Deemed to be University, Hyderabad, India Description An account of the resource Early detection of chronic diseases like diabetes is very important for early treatment and effective management. This chapter describes a machine learning (ML) solution for predicting diabetes risk from clinical structured data and a case study is constructed on the PIMA Indian Diabetes dataset. The solution caters to the entire ML pipeline: problem formulation, preprocessing of data, feature selection (FS), model training, validation, and deployment issues. Different preprocessing techniques including missing value imputation, detection of outliers, and feature normalization were used for improving data quality. FS techniques like correlation analysis, recursive feature elimination, and selection based on domain knowledge were utilized to decrease the dimensionality of the data as well as model interpretability. Extensive comparison was conducted among widely used classification models like logistic regression (LR), random forest, support vector machine, and XGBoost. It was suggested to adopt a stacked ensemble model of LR, RF, SVM, and XGBoost that achieved better performance in terms of accuracy, precision, recall, and F1-score. The findings confirm the tremendous potential of ML to enable early diabetes diagnosis as an unobtrusive, data-driven, and scalable decision-making supporting system for physicians. This is the groundwork for the further development of clinically applicable artificial intelligence-based prediction models within real-world healthcare settings. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. Subject The topic of the resource Diabetic prediction; Logistic regression; Machine learning; Random forest; Stacked ensemble model; Support vector machine; XGBoost Publisher An entity responsible for making the resource available De Gruyter Relation A related resource ISBN: 978-311154123-5; 978-311154107-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kukreja, Jyoti; Poonia, Ramesh Chandra Title A name given to the resource Machine learning for healthcare Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Advances in Computers;Volume;143;pp.131-151 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/bs.adcom.2026.01.005" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/bs.adcom.2026.01.005</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030450280?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030450280?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kukreja J., New Delhi Institute of Management, Delhi, India; Poonia R.C., Department of Computer Science, Christ University, Delhi-NCR, India Description An account of the resource Machine learning currently drives healthcare innovation, enabling novelty in solving complex medical problems. This chapter will present an in-depth critical review of various machine learning techniques applicable in healthcare in general, focusing on practical applications and recent advancements. It will further discuss supervised and unsupervised learning to semi-supervised learning methods, thereby detailing their uses for disease prediction, segmentation of patients, and image analysis in medical science. Among the most important areas in ML includes data preprocessing and feature engineering issues in health-care datasets. This further includes treatments for missing data, dimensionality reduction, and class imbalance. This chapter also discusses extensive case studies with state-of-the-art approaches that give insight into how the ML approach is changing health care decision-making, increasing diagnostic precision, and improving patient outcomes. Interpretability, scalability, and the mitigation of bias are further discussed as some of the challenges in the implementation of ML in healthcare. Ethical considerations regarding the need to develop responsible AI in healthcare and regulatory compliance are also discussed. It aims to serve as a handbook for researchers, practitioners, and policy analysts operating at the intersection between ML and healthcare. 2026 Publisher An entity responsible for making the resource available Academic Press Inc. Relation A related resource ISSN: 652458; ISBN: 978-044331710-1; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dabral, Anushka; Sivakumar, R. Title A name given to the resource Machine Learning for Mental Health: A Sentiment Analysis Approach for Detecting Depressive Tendencies on LinkedIn During Layoffs Using RoBERTa Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.279-290 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7717-4_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_19</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000442424?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000442424?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dabral A., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India; Sivakumar R., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource In the present corporate set-up, layoffs are an unfortunate yet common occurrence. Such occurrences lead to loss of job security and can have direconsequences on an individual's mental health, leading to depression. Depression was a global health concern well before the current downsizing came into the picture. These trying times have acted as a catalyst for this illness that affects not just mental health but all aspects of an individuals life. The study investigates the use of sentiment analysis on LinkedIn data to identify and examine depressive tendencies among victims of layoffs. Web-scraped information was taken from LinkedIn profiles of individuals affected directly or indirectly by layoffs. RoBERTa, a transformers model, is used to classify people as depressed or not by evaluating sentiment and emotional cues. A comparison between four machine learning algorithms- Decision Tree, Logistic Regression, SVM, and Nae Bayes is drawn to check their ability to detect depression. The SVM classifier performed best with an accuracy of 95.59% and 83.52% with the CountVectorizer and TF-IDF feature selection methods, respectively. Sentiment analysis aids in this research by examining the melancholic undertones in the words and phrases used in texts authored by people affected by layoffs directly or indirectly. The knowledge gained from this research can significantly affect corporate initiatives, mental health services, and human resource practices during such challenging times. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Depression; Layoffs; LinkedIn; Machine Learning; RoBERTa; Sentiment Analysis; TF-IDF Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981977716-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning for Smart School Selection and Admissions Subject The topic of the resource Admissions; Artificial Intelligence; Data-driven Decision Making; Education Technology; Educational Data Analysis; Machine Learning; Parental Decision-Making; Recommendation System; School Selection; Smart School Description An account of the resource Choosing the best school for their kid is an important choice that parents must make, and it is sometimes stressful and unsure. Machine learning is a potential way to improve and streamline the admissions and school selection process in the current digital era. This study investigates the use of machine learning methods in the context of selective admissions and smart school selection. We propose a user-friendly, web-based tool in the early phases of our study that helps parents and guardians locate the ideal school for their kid by using machine learning algorithms. To provide individualized school recommendations, the platform gathers and analyses a range of data, such as extracurricular activity participation, academic achievement, regional preferences, and school reputation. This makes choosing a school easier and supports parents in making wise choices. This paper's second section explores the technical details of the machine learning techniques used, going into the nuances of feature selection, data preparation, and model assessment. We also draw attention to the difficulties and moral issues - such as maintaining impartiality and avoiding bias - that come with using machine learning to school selection. 2023 IEEE. Creator An entity primarily responsible for making the resource Praveenraj D.D.W.; Victor M.; Subramani K. Source A related resource from which the described resource is derived 2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAKM58659.2023.10449592" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAKM58659.2023.10449592</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187555470&amp;doi=10.1109%2FICCAKM58659.2023.10449592&amp;partnerID=40&amp;md5=2d1e3fe94ab15b88dd04772c2e4a8512" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187555470&amp;doi=10.1109%2fICCAKM58659.2023.10449592&amp;partnerID=40&amp;md5=2d1e3fe94ab15b88dd04772c2e4a8512</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039324-8 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Praveenraj D.D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Victor M., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Subramani K., School of Business and Management, Christ (Deemed to Be University), Bangalore, India