Analytical Results of Heart Attack Prediction Using Data Mining Techniques
Classification; Heart Attack Prediction; K-Means; Knn; Linear Regression; Logistic Regression; Machine Learning; Random Forest Classifier; Regression; Supervised Algorithms; Unsupervised Algorithms
In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy 2023 IEEE.
Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications
Institute of Electrical and Electronics Engineers Inc.
Abhijith S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Mishra S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Prathap B., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
A Space Vector Modulated Direct Torque Control of Induction Motor with Improved Transient Performance and Reduced Parameters Dependency
Direct torque control; Induction motor drives; Pulse width modulation; Space vector modulation
Direct torque control (DTC) of induction motors is hampered by high torque and current ripple. Integrating DTC with space vector pulse width modulation (DTC-SVPWM) is one of the frequently used approaches to solve this problem. However, it adds to the computational complexity, increases the number of necessary motor parameters needed for control scheme implementation, and also affects the transient performance of the induction motor; this approach compromises the robustness and simplicity of DTC scheme. To get around these restrictions, a novel control strategy is put forth in this paper. The suggested scheme enhances the steady-state performance and transient response of the motor while preserving the simplicity and robustness of the DTC scheme. To accomplish this, the proposed control scheme operates at varying switching frequencies during transient conditions and constant switching frequencies during steady-state. The suggested speed control method does not employ any rotating reference frame transformations or usage of many rotor parameters for computation, nor does it call for sector identification and operates with a single PI controller. The suggested topology also uses a bus-clamped PWM modulation technique, which lowers the average switching frequency to 2/3 times the actual switching frequency. Thus, switching losses are also decreased. Simulation results show the effectiveness of the proposed topology in enhancing the transient and steady-state performance of the induction motor. The results are compared with the traditional DTC and DTC-SVPWM scheme. 2023 IEEE.
Peter A.K.; Haneesh K.M.; Hrudhya Kurian C.
2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023
https://doi.org/10.1109/SeFeT57834.2023.10245340 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173610532&doi=10.1109%2fSeFeT57834.2023.10245340&partnerID=40&md5=df5ef9f2f8e3e48523befdce5ad57884
Peter A.K., School of Engineering and Technology, Christ University, Eee Department, Bengaluru, India; Haneesh K.M., School of Engineering and Technology, Christ University, Eee Department, Bengaluru, India; Hrudhya Kurian C., School of Engineering and Technology, Christ University, Eee Department, Bengaluru, India
Kho Kho Model: A Novel Technique for Efficient Handoff in Vehicular Ad-hoc Networks
DSRC; Handoff; IEEE 802.11p; Kho Kho Model; Mobility; NS-3; Vehicular Ad hoc Networks; WAVE
The highly mobile nature of VANET implies that the nodes involved are constantly disconnecting and reconnecting as they switch between access points or move out of the range of their access points. In such scenarios, seamless connectivity is essential, especially when emergency services are involved. Handoff is a process in wireless communication that takes care of the switching process that happens between access points whenever a mobile device moves from one point to another. In a dynamic scenario involving vehicular nodes, this switching needs to take place between a mobile node or a fixed access point (known as RSUs), as quickly as possible. To this end, this research work proposes a novel handoff method known as the Kho Kho Model - which is loosely based on the traditional Indian sport of the same name. The model groups together nodes that are moving in the same direction, thereby effectively reducing the amount of processing required to perform handoff for a set of nodes. The use of ANN have helped to improve handoff since it can help in making decisions quickly by making use of multiple parameters including signal strength, noise, direction, and others. To improve the efficiency of the proposed handoff model, RBFNN has been used in this research. The proposed model was implemented using NS-3 simulator. The results have shown that the proposed method has a slightly better improvement in the overall NRO, a reduced average delay and reduced jitter compared to the existing handoff method employed by the IEEE 802.11p standard. 2023 IEEE.
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
https://doi.org/10.1109/ICECCME57830.2023.10252563 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85174046124&doi=10.1109%2fICECCME57830.2023.10252563&partnerID=40&md5=15cf2f7930f7510e353cac68da50f1ac
Thomas L., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India; Sandeep J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bengaluru, India
Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study
CNN; Galaxy; Parallel CNN; ResNet; Star; VGG16
This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE.
Premanand N.; Tarun V.G.; Pawar S.; Jawakar D.N.; Utsav; Deepa S.; Jayapriya J.; Vinay M.
2023 International Conference on Data Science and Network Security, ICDSNS 2023
Premanand N., Christ University, Department of Computer Science, Bengaluru, India; Tarun V.G., Christ University, Department of Computer Science, Bengaluru, India; Pawar S., Christ University, Department of Computer Science, Bengaluru, India; Jawakar D.N., Christ University, Department of Computer Science, Bengaluru, India; Utsav, Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India
Artificial Intelligence Involvement in Graphic Game Development
Artificial Intelligence; Game Development; Natural Language Processing; Rule-based Artificial Intelligence; Unreal Engine
Games have always been a popular form of entertainment and with the advancements in technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the gaming industry. This research article aims to explore the various applications of AI in gaming and its impact on the industry and player experience. Unlike the typical straightforward nature of AI, this research paper takes a more human approach to discussing the topic. It delves into the evolution of AI in games and the various types of AI used in game development. These include rule-based AI, learning- based AI, and evolutionary AI, which have all contributed to the development of increasingly immersive gaming experiences. The benefits and challenges of using AI in games are also explored, considering the impact on player experience. While AI-powered opponents can provide a greater challenge, balancing the difficulty level is critical to ensuring the game remains enjoyable. The potential ethical concerns of using AI in games are also discussed, such as data privacy, bias, and fairness. Furthermore, this research paper looks into the future of AI in games and how it may shape the gaming industry and player experience in the years to come. With the continued development of AI techniques such as reinforcement learning and GANs, the possibilities for more immersive and engaging gaming experiences are endless. 2023 IEEE.
Antony S.; Sabari T.; Joshua R.I.; Jayapandian N.
Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 82-86.
Antony S., Christ University, Department of Cse, Bangalore, India; Sabari T., Christ University, Department of Cse, Bangalore, India; Joshua R.I., Christ University, Department of Cse, Bangalore, India; Jayapandian N., Christ University, Department of Cse, Bangalore, India
HumanComputer Interactions with ArtificialIntelligence and Future Trends of HCIA Study
Artificial intelligence; Fuzzy logic; Humancomputer interaction; Machine learning
Artificial Intelligence, the name itself depicts the meaning that providing the knowledge of human to the machine artificially. AI is not a sense or feeling but the software or a model evolved to do complex tasks like human beings. With the invention of computer it has become so easy to do day to day jobs without much effort. HCI is all about interacting with computers. Now-a days it is possible to mesh with the computer through voice, touch, eye movement, and hand gestures. HCI has many challenges but has established in grand manner with the support of Artificial Intelligence. This study provides some important roles of Artificial Intelligence in HCI and its future development. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Muthumari S.; Sangeetha G.; Venkata Lakshmi S.; Suganthi J.
Lecture Notes in Networks and Systems, Vol-757 LNNS, pp. 1089-1100.
Springer Science and Business Media Deutschland GmbH
Muthumari S., S. S. Duraisamy Nadar Mariammal College, Tamil Nadu, Kovilpatti, India; Sangeetha G., Arulmigu Subramania Swamy Arts and Science College, Tamil Nadu, Vilathikulam, India; Venkata Lakshmi S., K.L.N College of Engineering, Madurai, India; Suganthi J., School of Sciences, Christ (Deemed to be) University, Bengaluru, India
Data Analysis on Hypothyroid Profiles using Machine Learning Algorithms
Hypothyroid; K-Nearest Neighbor; Logistic Regression; Long Short-Term Memory; Random Forest; XBG Classifier
Machine learning algorithms enable computers to learn from data and continuously enhance performance without explicit programming. Machine learning algorithms have significantly improved the accuracy and efficacy of thyroid diagnosis. This study identified and analysed the usefulness of several machine-learning algorithms in predicting hypothyroid profiles. The main goal of this study was to see the extent to which the algorithms adequately assessed whether a patient had hypothyroidism. Age, sex, health, pregnancy, and other factors are among the many factors considered. Extreme Gradient Boosting Classifier, Logistic Regression, Random Forest, Long-Term Memory, and K-Nearest Neighbors are some of the machine learning methods used. For this work, two datasets were used and analysed. Data on hypothyroidism was gathered via DataHub and Kaggle. These algorithms were applied to the collected data based on metrics such as Precision, Accuracy, F1 score and Recall. The findings showed that the Extreme Gradient Boosting classification method outperformed the others regarding F1 score, accuracy, precision, and recall. The research demonstrated how machine learning algorithms might predict thyroid profiles and identify thyroid-related illnesses. 2023 IEEE.
Leisha R., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India; Medows K., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India; Prathap B., CHRIST (Deemed to Be University), Computer Science and Engineering, Bangalore, India
Diabetic Retinopathy Diagnosis Using Retinal Fundus Images through MobileNetV3
Convolutional Neural Network; Diabetic Retinopathy (DR); MobileNet-V3; Proliferative; Retinal Fundus Image
Diabetic Retinopathy (DR) is a major disease throughoutthe world. Diagnosis of diabetes at an early stage is so critical and could help save several lifestyles. One out of two individuals experiencing diabetes has been determined to have some phase of DR. Recognition of DR symptoms in time can turn away the vision weakness inmost the cases, nonetheless, such disclosure is troublesome with present devices and strategies. Existingmethods for determining whether a person is suffering from diabetes or maybe the chances of acquiring diabetesrely heavily on examiners. Most of the time, it can be treated if caught during the early stages. There is a need for creating models that are efficient and robust to detect DR holistically. In recent times the advent of Deep learning models has been used extensively in various Bio medical applications. In this work, we utilize a Hyper parameter tuned MobileNet-V3 model based on a multi-stage Convolutional Neural Network (CNN) to efficiently classify images from the IRDID dataset. A Multiclass classification model involving images collated from various sources were trained, validated and tested for classification accuracy. The network was evaluated based on parameters and the network was able to achieve an accuracy of 88.6% 2023 IEEE.
Sejal A.Y., CHRIST(Deemed to Be University), Computer Science and Engineering, Bengaluru, India; Thiruthuvanathan M.M., CHRIST(Deemed to Be University), Computer Science and Engineering, Bengaluru, India
Data Analysis and Machine Learning Observation on Production Losses in the Food Processing Industry
Dairy Products; Data Analysis; Machine Learning; Production Loss; Total Productive Maintenance
Food wastage and capturing lineage from production to consumption is a bigger concern. Yielding, storage and transportation areas have evolved to a great extent associated to manufacturing and automation which lead to technical advancements in food processing industry. In such situation, losses are generally observed in the crop production which are sometimes minimal and ignored. However, in some cases these losses are huge and are becoming a threat to the both producers and consumers. Here we considered data related to dairy products and analysed the production losses especially while processing them in the treating unit. Literature on parameters and associated data analysis in the form of graphical representation are provided in the appropriate sections of the paper. Linear regression and correlation were envisaged in view of incorporating machine learning techniques understanding production losses. Karl Pearson's correlation provides an observation related to association of parameters which are desired to be less coupled in terms of employing proposed newer methodology. 2023 IEEE.
Kumar K.P.; Murthy H.; Pillai V.J.; Prathap B.R.; Moses M.; Urs Y.
Kumar K.P., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Murthy H., CHRIST Deemed to Be University, Department of Electronics and Communication Engineering, Bengaluru, Karnataka, India; Pillai V.J., CHRIST Deemed to Be University, Department of Electronics and Communication Engineering, Bengaluru, Karnataka, India; Prathap B.R., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Moses M., CHRIST Deemed to Be University, Department of Computer Science and Engineering, Bengaluru, Karnataka, India; Urs Y., CHRIST Deemed to Be University, Department of Civil Engineering, Bengaluru, Karnataka, India
Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model
Accuracy; Brain tumor; CNN; Custom CNN; ResNet50; VGG 16
Brain tumors pose a severe threat to global health and may be lethal. Early detection and classification of brain tumors are essential for successful therapy and better patient outcomes. The good news is that advances in deep learning techniques have shown tremendous promise in medical image analysis, particularly in the detection and classification of brain tumors. Convolutional Neural Networks (CNN), a class of deep learning models, are used to process and analyze visual input, notably images, and movies. They excel in computer vision tasks like object detection, image segmentation, and categorization. Popular and efficient image analysis methods include CNNs. VGG 16 and ResNet 50 are two examples of deep convolutional neural network architectures used for image categorization applications. A number of image identification problems have been successfully solved using the 16 layer VGG 16. ResNet50, a well known 50 layer architecture, employs residual connections to get over the vanishing gradient issue and permits the training of deeper networks. A proprietary CNN model, VGG 16, and ResNet50 were compared in studies to see how well they performed on a dataset. The VGG 16, ResNet50, and the tailored CNN model were the most precise models. As a consequence, VGG 16 accurately detects brain cancers in the dataset that was supplied. Overall, this study highlights the value of deep learning techniques for medical image processing and their potential to improve the accuracy and efficacy of brain tumor diagnosis and treatment. 2023 IEEE.
Sharma A.; Zehra A.; Das A.; Rastogi K.; Agarwal M.; Mascarenhas S.; Jayapriya J.; Vinay M.; Deepa S.
Sharma A., Christ University, Department of Computer Science, Bengaluru, India; Zehra A., Christ University, Department of Computer Science, Bengaluru, India; Das A., Christ University, Department of Computer Science, Bengaluru, India; Rastogi K., Christ University, Department of Computer Science, Bengaluru, India; Agarwal M., Christ University, Department of Computer Science, Bengaluru, India; Mascarenhas S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India
Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
Market research; Marketing; Power Sector; Production; Strategy
Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE.
Roy A.; Shete A.S.; Tiwari A.; Paliwal M.; Singh S.; Vatsa M.
2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023
Roy A., University of Lucknow, Institute of Management Sciences, Lucknow, India; Shete A.S., Krishna Vishwa Vidyapeeth, Krishna Institute of Pharmacy, Karad, India; Tiwari A., Christ (Deemed to Be University), School of Business and Management, Greater Noida, India; Paliwal M., Sri Balaji University, Balaji Institute of International Business(BIIB), Pune, India; Singh S., Sharda University, Sharda School of Business Studies, Greater Noida, India; Vatsa M., Akhildev Ipr and Research Services, Research and Development(R&D), Greater Noida, India
Facial Emotion Detection Using Deep Learning: A Survey
Convolutional Neural Network; Deep Learning; Facial Emotion
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE.
Ansari S.; Kulkarni P.; Rajesh T.; Gurudas V.R.
Ansari S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kulkarni P., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Rajesh T., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Gurudas V.R., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
Enhanced Design and Performance Analysis of a Seven-Level Multilevel Inverter for High-Power Applications
The structure and performance analysis of a seven-level multilevel inverter is discussed in this study. Due to their capacity to get around the drawbacks of traditional two-level inverters, like high voltage stress on power devices and harmonic distortion, multilevel inverters have attracted a lot of attention lately. Multiple voltage levels can be produced by the seven-level multilevel inverter which is being proposed because it uses a sequential arrangement of power sources and capacitors. The design methodology involves selecting appropriate power devices and capacitance values to achieve the desired voltage levels while minimizing losses and ensuring reliable operation. Total harmonic distortion (THD), inverter efficiency, and voltage stress on power devices are all considered as part of the performance analysis. In comparison to conventional two-level inverters, simulation results indicate that the proposed seven-level multilevel inverter offers lower THD, increased efficiency, and reduced voltage stress. This research contributes to the advancement of multilevel inverter technology and its potential applications in various power conversion systems. 2023 IEEE.
Parimalasundar E.; Jayakumar S.; Nagajothi P.; Chandrika V.S.; Devaraju T.; Suresh K.
Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 1862-1868.
Parimalasundar E., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Nagajothi P., S.A. Engineering College, Electrical and Electronics Engineering, Chennai, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Devaraju T., Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India
Student's Performance Prediction Using Modified PSO
Bag-of-features; Machine Learning Algorithm; Optimization techniques; Student Performance
In the present-day education system, evaluating students' performance is very important. The prediction of student performance is helpful to students and instructors in maintaining a student's progress. Presently, institutions are implementing Continuous Evaluation Systems. This system is beneficial for improving the students' performance and helps increase their regularity in their studies. This work proposed a new version of the particle swarm optimization algorithm to classify students into different categories based on their performance with the help of various factors. The proposed model modified the strategy to increase the optimization algorithm's efficiency. This model deals with the bag of features with the modified optimization algorithm to recognize students' performance based on 33 features. These features are used for training, validation, and testing of the student's performance and generate a classification report which includes F-measure, recall, precision, and accuracy. Based on the classification report, the proposed model is compared with the existing methods, and the better performance of the proposed model is ascertained. 2023 IEEE.
Proceedings - 2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023, pp. 1015-1020.
Kumari R., Icfai Business School (IBS) Bangalore, Off-Campus Center of Icfai Foundation for Higher Education (IFHE) University, Bangalore, India; Kumar S., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
Efficient Integration of Photovoltaic Cells with Multiport Converter for Enhanced Energy Harvesting
Battery; Efficiency; Multiport converter; PV Cell
This research work presents a novel approach for the efficient integration of photovoltaic (PV) cells with a multiport converter to enhance energy harvesting in renewable energy systems. The proposed system combines the advantages of PV technology with the flexibility and scalability of multiport converters, enabling improved power extraction and utilization from solar energy sources. The integration is achieved by employing a multi-input multi-output (MIMO) control strategy, which optimally distributes power among multiple energy storage systems and loads. A comprehensive modeling and analysis of the PV cell characteristics and the multiport converter are conducted to identify the optimal operating conditions. Furthermore, a power management algorithm is developed to dynamically regulate the power flow and maximize the energy harvesting efficiency. The proposed approach demonstrates superior performance compared to traditional single-input single-output converters, achieving higher energy yields and enabling effective integration of PV cells in diverse applications. Simulation results validate the effectiveness of the proposed approach, showcasing its potential to significantly enhance energy harvesting from photovoltaic sources and contribute to the development of sustainable and reliable renewable energy systems. 2023 IEEE.
Parimalasundar E.; Jayakumar S.; Chandrika V.S.; Muthukaruppasamy S.; Devaraju T.; Suresh K.
Proceedings of the 5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023, pp. 1590-1595.
Parimalasundar E., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Jayakumar S., Sri Sairam College of Engineering, Electronics and Communication Engineering, Bengaluru, India; Chandrika V.S., Kpr Institute of Engineering and Technology, Electrical and Electronics Engineering, Coimbatore, India; Muthukaruppasamy S., Velammal Institute of Technology, Electrical and Electronics Engineering, Chennai, India; Devaraju T., Mohan Babu University, Erst while Sree Vidyanikethan Engineering College, Electrical and Electronics Engineering, Tirupati, India; Suresh K., Christ University, Electrical and Electronics Engineering, Bangalore, India
Analytical Methods of Machine Learning Model for E-Commerce Sales Analysis and Prediction
Boosting; Explained Variance; Extreme Gradient Boosting; Machine Learning; Sales Prediction
In the commercial market, E-commerce sales show a significant trend and have attracted many consumers. Ecommerce sales forecasting has a significant role in an organization's growth and aids in improved operation. Many studies have been conducted in the past using statistical, fundamental, and data mining techniques for better analysis and prediction of sales. However, the current scenario calls for a better study that combines the available information to propose different machine-learning techniques. The sole motive of the study is to analyze and determine different machine learning models to predict accurate results. The research observed that the Extreme Gradient Boosting model outperformed all other models and brought a good result. It produced an RMSE value of 0.0004 and Explained Variance score of 0.99. Decision Tree algorithm also shows an exemplary result. 2023 IEEE.
Anushka Xavier K.; Manjunath C.; Manohar M.; Gurudas V.R.; Jayapandian N.; Balamurugan M.
Anushka Xavier K., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Manjunath C., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Manohar M., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Gurudas V.R., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Jayapandian N., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India; Balamurugan M., CHRIST (Deemed to Be University), Department of CSE, Bangalore, India
Machine Learning Model to Detect Chronic Leukemia in Microscopic Blood Smear Images
Chronic lymphocytic Leukemia; Chronic Myeloid Leukemia; image processing; Machine Learning; Random Forest; Watershed Segmentation
Chronic leukemia is a slow-progressing form of disease, If not diagnosed on time can progress and increase the risk of life-threatening complications. It is essential to develop a fully automated system to recognize and categorize type of leukemia for proper evaluation and treatment. This paper aims to provide a machine learning model to identify and classify chronic lymphocytic leukemia, chronic myeloid Leukemia and healthy cells. Digital microscopic blood smear images were automatically cropped into single nucleus and segmented using watershed algorithm. Grey level co-occurrence matrix (GLCM) and geometrical features were extracted from the segmented nucleus images and random forest algorithm is used to classify chronic leukemia and healthy cells. This prognosis aids pathologists and physicians in identifying leukemic patients early and selecting the most effective course of action. 2023 IEEE.
Patil A.P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, India; Hiremath M., CMR Institute of Technology, Department of Computer Application, Bengaluru, India
Role of Machine Learning in the Analysis of Mental Health Data: An Empirical Approach
Machine learning; Mental health; Supervised learning; Unsupervised learning
As funding for mental health research has grown, so too has the body of knowledge about how best to address and alleviate issues related to mental health. However, there is still a lack of certainty and clarity on the precise causes of mental diseases. Discovery of new drugs, analysis of radiological data, forecasting of disease outbreaks, and the diagnosis of illnesses are just some of the medical applications of machine learning algorithms. Machine learning algorithms are commonly used to sift through the mountains of medical data. Since their performance has improved to the point where it can be relied upon, they are now used to aid in medical diagnosis. To assess and address the issues with mental health, numerous new approaches and algorithms had been devised. There are still a lot of issues that can be resolved. So the main purpose of this study is to examine the effectiveness of machine learning in mental health problems. For fulfilling this purpose, this study is descriptive in nature. Primary data is collected with the help of interview method in which 50 individuals suffering from mental illness were asked to answers some questions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Hashmi S.G.; Shahi F.I.; Qidwai K.A.; Naser M.; Shafiuddin M.; Upreti K.
Lecture Notes in Networks and Systems, Vol-730 LNNS, pp. 513-522.
Hashmi S.G., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia; Shahi F.I., Deanship of E-Learning and Information Technology, Jazan University, Jazan, Saudi Arabia; Qidwai K.A., Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jizan, Saudi Arabia; Naser M., Deanship of E-Learning and Information Technology, Jazan University, Jazan, Saudi Arabia; Shafiuddin M., AFS Department, Oman College of Management and Technology, Halban, Oman; Upreti K., Department of Computer Science CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India
Smart Intravenous Infusion Monitoring and Alert System using IoT-based Force Sensitive Resistor and Whatabot API
FSR sensor; Healthcare; IoT; IV fluid; NodeMCUESP8266; Whatabot
Intravenous fluids with vitamins are given to people who are dehydrated and have an imbalance of electrolytes. When the IV bag is empty, the patient's blood flows through the IV line toward the empty bag because their blood pressure is higher than the IV bag's. This process, called diffusion can lead to pain and loss of blood. If the IV bag is empty and hooked up to the patient, air can get into the bloodstream. The air bubble enters the patient's bloodstream, causing the same catastrophic effects. This paper aims to eliminate the danger by creating Smart IV Bags in light of the rising number of risks in the medical sector brought on by the reverse flow of blood in an IV bag (intravenous bag). The Smart IV bags eliminate the need for constant physical monitoring of the IV bag's state while preventing reverse blood flow. For detection of the IV fluid state, a 0.5 ' diameter force-sensitive resistor is deployed. We integrate the system with a NodeMCU module and using Wi-Fi, it establishes communication between the smart IV bag and the person in charge at the hospital, like a nurse or a caretaker. In this study, the IV Bag is the 'thing' connected to the internet where the FSR readings are analyzed using NodeMCU script to determine if it needs to be emptied and send a WhatsApp message to the caretaker. This design establishes an IoT environment where the IV Bag automatically alerts the caretaker and eliminates the need for constant human intervention. 2023 IEEE.
Sreekumar K., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Francis J., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India; Prathap B.R., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bengaluru, India
KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm
KnowSOntoWSR; QoS ontology; Twitter semantic similarity; Web service recommendations; XGBoost
Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models estimates with an accuracy of 95.94% and average F-measure of 95.93%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Dhanvardini R.; Deepak G.; Priyadarshini J.S.; Santhanavijayan A.
Lecture Notes in Networks and Systems, Vol-616 LNNS, pp. 233-242.
Dhanvardini R., Healthcare Informatics Division, Optum UnitedHealth Groups, Hyderabad, India; Deepak G., Department of Computer Science Engineering, National Institute of Technology, Tiruchirappalli, India; Priyadarshini J.S., Department of Data Science, Manipal Institute of Technology Bengaluru, Bengaluru, India, Department of Data Science, Manipal Academy of Higher Education, Manipal, India, CHRIST (Deemed to Be University), Bangalore, India; Santhanavijayan A., Department of Computer Science Engineering, National Institute of Technology, Tiruchirappalli, India