An Efficient Quantum Transfer Learning for Cancer Prediction Using Tumour Markers: New Era of Computer in Medical
accuracy; antigen; biomarkers; diagnosis; ovarian cancer; prediction; prognosis; Quantum learning; transfer learning; tumour
Ovarian cancer prediction models or algorithms estimate a person's risk of getting the disease based on different variables, such as their medical history, genetics, and biomarkers. Early identification and intervention will enhance patient successive diagnosis outcomes. Tumour markers are chemicals frequently detected in higher concentrations than usual in cancer patient's blood, urine, or tissues. They could be certain chemicals or proteins linked to the presence of tumours or cancer kinds. Tumour markers are employed for diagnosis, prognosis, and treatment response monitoring. Applying information or models from one quantum job to enhance the performance of another requires quantum transfer learning. Transferring knowledge from one domain to another seeks to increase learning effectiveness in novel quantum contexts. The main goal of efficient Quantum Transfer Learning (QTL) is to minimize the resources (computer power, data, or time) necessary to transfer between tasks successfully. In this research work, QTL is used to predict Ovarian Cancer (OC) with the assistance of biomarkers. The Quantum Transfer Learning- Ovarian Cancer (QTL-OC) achieves 93.78% accuracy and outperforms the existing techniques. 2023 IEEE.
Narendran M.; Andi H.K.; Sharma D.K.; Amarendra K.; Uma S.; Poonia R.C.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 432-437.
Institute of Electrical and Electronics Engineers Inc.
Narendran M., Kcg College of Technology, Department of Information Technology, Tamil Nadu, Chennai, India; Andi H.K., Asia Metropolitan University, Centre for Postgraduate Studies, Malaysia; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Amarendra K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Andhra Pradesh, Vaddeswaram, 522502, India; Uma S., Dhanalakshmi Srinivasan College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, Mamallapuram, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India
Enhancing Retailer Auctions and Analyzing the Impact of Coupon Offers on Customer Engagement and Sales Through Machine Learning
Classification; Coupon Campaign; Coupon Code; Customer Behavior; Machine Learning; Prediction
Systems that use coupons have been used extensively to boost customer interaction on platforms having a digital component. We use causal machine learning techniques to determine the effect of an advertising intervention, especially a discount offer, on the bids of a shop. Discount shopping coupons are a popular tactic for increasing sales. The largest challenge for dealers is accurately anticipating the wants of their customers, and here is where they always struggle. Machine learning algorithms have been utilized by researchers to address a variety of problems. Selecting the right coupon is a challenging undertaking because every customer's behavior differs depending on the deal. Due to categorical data adjustments being necessary due to the majority of characteristics having missing values, the situation is made more difficult. The dataset is used to classify the dataset, and machine learning algorithms like logistic regression, random forest and SVM model, decision tree and naive bayes models are used to determine the correctness of the classification. 2023 IEEE.
Jayadharshini P.; Sharon Roji P.C.; Lalitha K.; Santhiya S.; Keerthika S.; Abinaya N.
2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023
Jayadharshini P., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Sharon Roji P.C., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Karnataka, Bangalore, India; Lalitha K., School of Engineering and Technology, Christ(Deemed to Be) University, Department of Cse, Bengaluru, India; Santhiya S., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Keerthika S., Department of Artificial Intelligence, Kongu Engineering College, Tamilnadul, Erode, India; Abinaya N., Kongu Engineering College, Department of Artificial Intelligence, Tamilnadul, Erode, India
Loan Default Prediction Using Machine Learning Techniques and Deep Learning ANN Model
Artificial Neural Network; Kaggle; Lending Club; Loan prediction
Loan default prediction is a critical task in the financial sector, aimed at assessing the creditworthiness of borrowers and minimizing potential losses for lending institutions. Online loans continue to reach the public spotlight as Internet technology develops, and this trend is expected to continue in the foreseeable future. In this paper, the authors proposed loan default loan prediction system based on ML and DL models. This work makes use of the information on loan defaults provided by Lending Club. The dataset is preprocessed by applying various data preprocessing techniques and preprocessed dataset is generated. Later, we proposed four ML algorithms decision tree, random forest, logistic regression, K-NN and Feed forward neural network. The experimental results shown that proposed feed forward neural network achieved good accuracy for loan default prediction with an accuracy of 99%. 2023 IEEE.
Lakshmanarao A.; Gupta C.; Koppireddy C.S.; Ramesh U.V.; Dev D.R.
2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023
https://doi.org/10.1109/AICERA/ICIS59538.2023.10420221 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186138850&doi=10.1109%2fAICERA%2fICIS59538.2023.10420221&partnerID=40&md5=71a6f3f031b8dcaa2b19d525f5a9f984
Lakshmanarao A., Aditya Engineering College, Department of It, Surampalem, India; Gupta C., School of Commerce, Finance and Accountancy, Christ University, India; Koppireddy C.S., Pragati Engineering College, Cse Department, A.P, Surampalem, India; Ramesh U.V., Aditya Engineering College, Department of Cse, Surampalem, India; Dev D.R., Vignan's Institute of Engineering for Women, India
A Gated Recurrent Unit Based Continual Normalization Model for Arrythmia Classification Using ECG Signals
Cardiac arrhythmias; Continual normalization; Electrocardiogram; Gated Recurrent Unit; MIT-BIH
In this world, around 31% of the deaths are commonly caused because of cardiovascular diseases. Around 80% of sudden deaths occur due to cardiac arrhythmias and heart diseases. The mortality rate has increased for cardiac disease and therefore early heart disease detection is significant to preclude patients from dying. At the initial phase, the heart disease is detected by analyzing abnormal heartbeats. The existing models failed to select the features before performing the extraction of features. The developed model examined MIT-BIB database to surpass the overfitting issue. Therefore, in the present research work, the Gated Recurrent Unit (GRU) based Continual Normalization (CN) classifier is used to speed up the training to a higher learning rate to enable simpler learning for the standard deviation of the neurons' output. The extracted features were used to classify Electrocardiogram (ECG) signals into 5 important classes named as N, S, V, F & Q which denote the kinds of arrhythmia. The findings revealed that the proposed GRU based Continual Normalization technique obtained an accuracy of 99.41% which is better when compared with the existing researches. 2023 IEEE.
Manjunath C.; Hemalatha K.L.; Sandya V.; Sunil G.; Shakir A.M.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Manjunath C., CHRIST(Deemed to Be University), Department of Cse, Bengaluru, India; Hemalatha K.L., Sri Krishna Institute of Technology, Department of Information Science and Engineering, Bengaluru, India; Sandya V., Cmr Technical Campus, Department of CSE(Data Science), Hyderabad, India; Sunil G., School of Computer Science & Artificial Intelligence, Sr University, Warangal, India; Shakir A.M., The Islamic University, Najaf, Iraq
Leveraging the Synergy of Edge Computing and IoT in Supply Chain Management
Edge Computing; IoT; Supply Chain Management
This article investigates the possibilities of integrating edge computing and IoT in supply chain management, as well as the adoption of disruptive technologies such as blockchain integration, digital twins, robotics, and autonomous systems. Operational efficiency can be considerably enhanced by establishing a linked and intelligent supply chain ecosystem. The benefits of this technology include increased openness, efficiency, and resilience in supply chain processes. Among the benefits include real-time product tracking, environmental sustainability, enhanced production, and cost savings. The use of blockchain technology in a three-tiered Supply Chain Network (SCN) shows promise in terms of boosting supply chain transparency and security. The SCOR model is also discussed as a comprehensive framework for optimising supply chain processes. However, concerns such as data privacy, security, and employment displacement must be solved before firms can fully reap the benefits of new technologies. Overall, embracing these innovations has the potential to revolutionise supply chain management and create trust among stakeholders. 2023 IEEE.
Sharma V.; Mahanayak S.P.; Thapa T.; Mishra S.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1055-1062.
Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Mahanayak S.P., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Thapa T., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Election Forecasting with Machine Learning and Sentiment Analysis: Karnataka 2023
BJP; Election Prediction; Ensemble Learning; INC; JDS; Karnataka Election; Machine Learning; Political Analysis; Predictive Modelling; Sentiment Analysis
Data science is rapidly transforming the political sphere, enabling more informed and data- driven electoral processes. The ensemble machine model which is made up of Random Forest Classifier, Gradient Boosting Classifier, and Voting Classifier, introduced in this paper makes use of machine learning methods and sentiment analysis to correctly forecast the results of the Karnataka state elections in 2023. Election features such as winning party, runner- up party, district name, winning margin, and voting turnout are used to evaluate the effectiveness of different machine learning paradigms. Similarly, it also makes use of sentiment analysis through party tweet and public reactions for further breaking down reliance upon past elections data alone. This study demonstrates that using both past historical records and current public opinion yields precise predictions about how electable leaders are. This reduces reliance on a historical dataset. The experimented results shows that, how machine learning and sentiment analysis can predict election results and provide useful data for election decision making. We compared various machine learning models in this study, including logistic regression, Grid SearchCV, XGBoost, Gradient Boosting Classifier, and ensemble model. With an accuracy of 85%, we demonstrated that our ensemble model outperformed machine models such as XGBoost and Gradient Boosting Classifier. It also offers a novel method for predictive analysis. 2023 IEEE.
Gohil R.; Deepa S.; Vinay M.; Jayapriya J.
https://doi.org/10.1109/AIKIIE60097.2023.10390333 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187269175&doi=10.1109%2fAIKIIE60097.2023.10390333&partnerID=40&md5=248596f3cf47caf9716119faaf24a5bb
Gohil R., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Deepa S., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Vinay M., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India; Jayapriya J., Christ (Deemed to Be University), Department of Computer Science, Karnataka, Bangalore, India
An Empirical Examination of the Factors of Big Data Analytics Implementation in Supply Chain Management and Logistics
Big data analytics; Logistics; Operational efficiency; Supply chain management
Numerous companies have effectively exploited Big Data Analytics (BDA) potential to enhance their effectiveness in the Big Data period. Given that big data application in logistics and supply chain management (SCM) is nevertheless in its early stages, assessments of BDA could differ from various viewpoints, producing certain difficulties in comprehending the significance and potential of big data. Based on past research on BDA and SCM, this work examines the factors that influence organizations' willingness to implement BDA in their everyday activities. This research divides potential elements into 4 groups: technical, firm, ecological, and supply chain issues. A framework consisting of direct factors like technical, firm, and mediators was presented based on the technology diffusion hypothesis. The experimental findings demonstrated that anticipated advantages and high-level management assistance might have a considerable impact on intended adoption. Furthermore, ecological variables like competitive adoption, administration legislation, and supply chain connection can greatly alter the direct connections between influencing causes and intended adoption. 2023 IEEE.
Ravindar K.; Syed M.H.; Sampath B.; Rajalakshmi B.; Reddy V.V.; Dutt A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 452-456.
https://doi.org/10.1109/UPCON59197.2023.10434540 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187337065&doi=10.1109%2fUPCON59197.2023.10434540&partnerID=40&md5=ed79aacb545f2cfe4a4038c2cf06fd1a
Ravindar K., School of Sciences, Sr University, Telangana, Warangal, India; Syed M.H., Saudi Electronic University, Department of Computer Science, Saudi Arabia; Sampath B., School of Business Management, Christ(Deemed to Be) University, Karnataka, Bangalore, India; Rajalakshmi B., New Horizon College of Engineering, Department of Computer Science, Bangalore, India; Reddy V.V., Institute of Aeronautical Engineering, Dundigal, Hyderabad, India; Dutt A., Lovely Professional University, Phagwara, India
Automated Leukaemia Prediction and Classification Using Deep Learning Techniques
accuracy; activation function; and associative memory; bone marrow; classification; DL; Leukaemia; neural network; neurons
Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. 2023 IEEE.
Narendran M.; Amarendra K.; Revathy D.; Divyapushpalakshmi M.; Sharma D.K.; Upreti K.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 420-425.
Narendran M., Kcg College of Technology, Department of Information Technology, Tamil Nadu, Chennai, India; Amarendra K., Koneru Lakshmaiah Education Foundation, Department of Computer Science and Engineering, Andhra Pradesh, Vaddeswaram, 522502, India; Revathy D., Karpaga Vinayaga College of Engineering and Technology, Department of Computer Science and Engineering, Tamil Nadu, Chengalpet, India; Divyapushpalakshmi M., Gitam University, Department of Computer Science and Engineering, Bengaluru, 561203, India; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, Delhi -NCR, Ghaziabad, India
An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach
Convolutional Neural Network (CNN); Election prediction; Natural Language Processing (NLP)
Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have been used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superiorto the widely usedBiCNN and RNN methods. 2023 IEEE.
Acharjee P.B.; Magadum A.A.; Thejovathi M.; Jain R.; Umarani K.; Nishant N.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 765-770.
https://doi.org/10.1109/ICACRS58579.2023.10404211 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185388377&doi=10.1109%2fICACRS58579.2023.10404211&partnerID=40&md5=7c6e995139838d67dee2419beecddb30
Acharjee P.B., CHRIST University, Pune, India; Magadum A.A., Department of MCA, MIT-ADT University, Maharastra, Pune, India; Thejovathi M., Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, India; Jain R., School of Arts & Humanities, IIMT University, Meerut, India; Umarani K., School of Arts & Humanities, IIMT University, Meerut, India; Nishant N., Department of Computer Science and Engineering, School of Engineering, Babu Banarasi Das University, Lucknow, India
Federated Learning and Blockchain: A Cross-Domain Convergence
blockchain; consensus protocols; Federated learning; machine learning; smart contracts
Gaining significant attention within decentralized contexts, Federated Learning (FL) has been positioned as a highly desirable method for machine learning. By enabling multiple entities to train a shared model cooperatively, data privacy and security are preserved by Federated Learning. Harnessing inherent transparency and accountability of blockchain technology to trace and authenticate updates effectively in federated learning has transpired as an up-and-coming avenue to tackle data challenges related to confidentiality, protection, and reliability. This study examines the viability of federated learning and blockchain integration across multiple dimensions. The technological components of this integration., including incentive systems, consensus mechanisms, data validation, and smart contracts, are delved into. In the study, a novel proposed model for federated learning integrated with blockchain is designed and implemented. It is observed that the mean cypher size is 100 bytes for varying values of gradients. The average throughput recorded is 1.7 bytes per second, while the mean accuracy is 87.1% for 50 epochs. 2023 IEEE.
Sharma S.; Pandey A.; Sharma V.; Mishra S.; Alkhayyat A.
Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 1121-1127.
https://doi.org/10.1109/ICTACS59847.2023.10390227 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185386871&doi=10.1109%2fICTACS59847.2023.10390227&partnerID=40&md5=d638a49d0fd38d2bf13e4e77e3e8a321
Sharma S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Pandey A., Oriental Institute of Science and Technology, Madhya Pradesh, Bhopal, India; Sharma V., Christ (Deemed to Be University), Delhi NCR, India; Mishra S., Christ (Deemed to Be University), Delhi NCR, India, Kalinga Institute of Industrial Technology, Deemed to Be University, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
A Comparative Analysis of Autonomous Ledger Systems for Enhanced Blockchain Computing Applications
Adaptive Consensus; Autonomous Ledger Systems; Blockchain Computing; Comparative Analysis; Decentralized Ledger; Efficiency; Resource Allocation; Scalability; Self-governance; Self-management
Due to its potential to completely transform a number of sectors thanks to its irreversible and decentralized ledger system, the use of blockchain technology has recently attracted a lot of interest. Blockchain-based systems still face considerable issues with regard to scalability and effectiveness. This study compares autonomous ledger systems and examines how they are used in blockchain technology computation. With their capacity for self-management and resource allocation optimization, autonomous ledger systems provide intriguing answers to these problems. As examples of autonomous ledger systems, we look at self-care networks, adaptable consensus techniques, and autonomous government systems. We compare them and assess how well they function to improve the speed, security, and scalability of blockchain networks. We also examine the real-world uses of these independent ledger systems in industries including logistics management, banking, and medical services. With the goal of advancing blockchain computing and enabling more reliable and effective decentralized applications, this study intends to shed light on the possibilities of autonomous ledger systems. 2023 IEEE.
Ghosh S.; Upreti K.; Jafri S.; Divakaran P.; Chaturvedi V.M.; Vats P.
16th International Conference on Security of Information and Networks, SIN 2023
Ghosh S., Maharaja Agrasen Institute of Technology, Department of It, Delhi, India; Upreti K., Christ (Deemed to Be University), Department of Computer Science, Delhi NCR, Ghaziabad, India; Jafri S., Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India; Chaturvedi V.M., D y Patil Deemed to Be University, Navi Mumbai, India; Vats P., Scse, Manipal University Jaipur, Faculty of Engineering, Dept. of Cse, Rajasthan, Jaipur, India
A Hybrid Edge-Cloud Computing Approach for Energy-Efficient Surveillance Using Deep Reinforcement Learning
Cloud Data Centers; Deep Reinforcement Learning; Edge Computing; Energy Efficiency; Smart Surveillance
This paper explores the novel application of Deep Reinforcement Learning (DRL) in designing a more efficient, scalable, and distributed surveillance architecture, which addresses concerns such as data storage limitations, latency, event detection accuracy, and significant energy consumption in cloud data centers. The proposed architecture employs edge and cloud computing to optimize video data processing and energy usage. The study further investigates the energy consumption patterns of such a system in detail. The implementation leverages machine learning models to identify optimal policies based on system interactions. The proposed solution is tested over an extensive period, resulting in a system capable of reducing latency, enhancing event detection accuracy, and minimizing energy consumption. 2023 IEEE.
Singh J.; Dabas P.; Bhati S.; Kumar S.; Upreti K.; Shaik N.
Proceedings - International Conference on Technological Advancements in Computational Sciences, ICTACS 2023, pp. 432-438.
Singh J., School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India; Dabas P., Dr Akhilesh das Gupta Institute of Technology and Management, Dept. of Computer Science & Engineering, Delhi, India; Bhati S., Jecrc University, Department of Computer Applications, Jaipur, India; Kumar S., Raj Kumar Goel Institute of Technology, Department of Information Technology, Ghaziabad, India; Upreti K., Christ University (Deemed to Be University), Department of Computer Science, Ghaziabad, India; Shaik N., SRIT-Autonomous Anantapur, Department of Computer Science Engineering, India
Privacy Optimization in Sensors Based Networks With Industrial Processes Management
Cybersecurity; Denial-of-Service (DDoS); Industrial Process Management; Integrated Security Framework; Internet of Things (IoT); Process Management
The Internet of Things (IoT) also known as IoT has the potential that is required to revolutionize industries, this has been discussed in this research article. Advancements in technology have made devices affordable, efficient and reliable. Different sectors have already started to incorporate these devices into their operations to boost productivity, to minimize failure and downtime. They also use it to optimize resource utilization which is also an important factor. However, the use of these devices also has some security challenges which need to be handled. This research paper proposes a security model specifically designed for process management in the industries. The goal of this model is to find the vulnerabilities, to minimize the risks and threats. Also ensuring integrity, confidentiality and availability of processes is a part of the goal. This paper gives evidence from its implementation and trial apart from its explanation. During the implementation phase, the sensitive data achieved a 100% encryption rate, for protection. Also, integrity checks were conducted on 99.8% of data to guarantee data integrity. 2023 IEEE.
Siddiqui A.F.; Mishra S.; Jena M.M.; Sharma V.; Al-Khasawneh M.A.; Alkhayyat A.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
https://doi.org/10.1109/ICTBIG59752.2023.10455985 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189239393&doi=10.1109%2fICTBIG59752.2023.10455985&partnerID=40&md5=2bfaa41e893062c3cfe6000d5b7fe145
Siddiqui A.F., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Jena M.M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Al-Khasawneh M.A., School of Computing, Skyline University College, University City Sharjah, United Arab Emirates; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
Decoupling Identification Method of Continuous Working Conditions of Diesel Engines Based on a Graph Self-Attention Network
Decoupling; Diseal Engine; Graph Self-Attention Network (GSAN); Normal and Typical Scenario; Signal Preprocessing; Working Condition
For diesel engine malfunction detection, machine learning-based intelligent detection approaches have made great strides, but some performance deterioration is also observed due to the significant ambient noise and the change in operating circumstances in the actual application situations. Diesel engine fault diagnostic models can be negatively impacted by complex and erratic working circumstances. Identifying the working condition can provide as a baseline for the current unit operating state, which is crucial information when trying to pinpoint the source of an issue. Many existing techniques for identifying operational states use power as an identifier, segmenting it into discrete intervals from which the current state's power may be derived using a classification model. However, the working condition characteristics should be constant, and defining it exclusively in terms of power would lead to the connection of speed and load elements. In this study, we offer a regular working situation model that is independent of speed and load characteristics, and we use a graph self-attention network to construct a model for identifying the working condition. On a diesel engine research bench, a vast amount of experimental data is acquired for training and testing models, including 32 different operating situations under normal and typical fault scenarios. The R2 adj values of 99.70% and 99.27% for normal and typical defect experimental data, correspondingly, demonstrate the efficacy of the suggested technique under the circumstance of uninformed nnerating situations. 2023 IEEE.
Vijayasharathi N.; Selukar N.B.; Ganesh Kumar G.; Chauhan A.; Ramachandran L.; Ravindra Sonawane P.
7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 49-54.
Vijayasharathi N., Panimalar Engineering College, Tamilnadu, Chennai, India; Selukar N.B., SGB Amravati University, Chemical Technology Department, Maharashtra, Amravati, India; Ganesh Kumar G., Kakatiya Institute of Technology and Science, Department of Mechanical Engineering, Telangana, Warangal, India; Chauhan A., Department of life sciences, Christ University Bangalore, Karnataka, India; Ramachandran L., R.M.K.Engineering College, Kavaraipettai, Tamilnadu, Tiruvallur, India; Ravindra Sonawane P., JSPM'S Rajarshi Shahu College of Engineering Tathawade, Mechanical Engineering, Maharashtra, Pune, India
A Deep Learning Methodology CNN-ADAM for the Prediction of PCOS from Text Report
Adam; Classification; CNN; deep learning; PCOS
Text categorization is a popular piece of work in natural language processing (NLP) and machine learning, and Convolutional Neural Networks (CNNs) can be used effectively for this purpose. Although CNNs are traditionally associated with computer vision tasks, they have been adapted and applied successfully to text classification problems. In the proposed study Convolutional Neural Networks (CNNs) with adam optimization algorithm plays a crucial role in detecting PCOS words from sonographic text reports. 2023 IEEE.
Thomas N.; Alapatt B.P.; Resmi K.R.; Jose M.
2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances: Sustainable Transportation Systems, CERA 2023
Thomas N., Santhigiri College, Kerala, India; Alapatt B.P., Christ (Deemed to Be University), Delhi-NCR, India; Resmi K.R., Christ (Deemed to Be University, Banglore), India; Jose M., Axis Bank, Chennai, India
Empirical study on The Role of Machine Learning in Stress Assessment among Adolescents
Adolescent; CNN; Deep learning; LSTM; Machine Learning algorithms; Stress
Stress is a psychological condition that people who are experiencing difficulties in their social and environmental well-being face, and it can cause several health problems. Young individuals experience major changes during this crucial time, and they are expected to succeed in society. It's critical for people to master appropriate stress management techniques to ensure a smooth transition into adulthood. The transition to new settings, lifestyles, and interactions with a variety of people, things, and events occurs during adolescence. In this study, a dataset was utilized to classify 520 Indian individuals' stress levels into three categories: normal, moderate, and severe. Support Vector Machines, KNN, Decision Trees, Naive Bayes and CNN were among the different classification techniques that were taken into consideration. The CNN Algorithm was found to be the most reliable method for categorizing diseases linked to mental stress. The study's main goal is to create a classification model that can correctly classify a variety of samples into distinct levels of psychological discomfort. 2023 IEEE.
4th International Conference on Communication, Computing and Industry 6.0, C216 2023
Rappai S., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Gobi R., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
Information Extraction Using Data Mining Techniques For Big Data Processing in Digital Marketing Platforms
Big data; Data mining; Digital marketing
In the dynamic landscape of digital marketing, harnessing the potential of big data has become paramount for informed decision-making. This study explores the integration of data mining techniques within big data processing frameworks to extract valuable information in digital marketing platforms. With the exponential growth of data generated through online interactions, social media, and e-commerce, traditional methods fall short of uncovering meaningful insights. This research focuses on leveraging advanced data mining algorithms to sift through vast datasets, identifying patterns, trends, and user behaviours. The proposed approach aims to enhance marketing strategies by extracting actionable intelligence from diverse data sources. Techniques such as association rule mining, clustering, and sentiment analysis will be employed to unveil hidden correlations, segment target audiences, and gauge consumer sentiment. The scalability of big data frameworks ensures efficient processing of massive datasets, allowing marketers to make real-time, data-driven decisions. Additionally, the study explores the challenges and opportunities associated with implementing data mining in big data environments for digital marketing. This research contributes to the evolving field of digital marketing by providing a comprehensive framework for extracting, processing, and utilizing information from big data. The findings promise to empower marketers with a deeper understanding of consumer behaviour, enabling the development of more personalized and effective marketing strategies in the ever-evolving digital ecosystem. 2023 IEEE.
Ganesh C.; Ramachandran K.K.; Varasree B.; Lakhanpal S.; Rohini B.; Acharjee P.B., Dr.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1662-1667.
Ganesh C., Kg Reddy College of Engineering & Technology, Mba, Telangana, Hyderabad, India; Ramachandran K.K., Dr G R D College of Science, Management/Commerce/International Business, India; Varasree B., Institute of Aeronautical Engineering, Telangana, Hyderabad, 500043, India; Lakhanpal S., Lovely Professional University, Phagwara, India; Rohini B., New Horizon College of Engineering, Department of Physics, Bangalore, 560103, India; Acharjee P.B., Dr., Christ University, Computer Science, Pune, India
Intelligent Safety Life Jacket Using Wsn Technology
The body loses heat in hypothermia because it cannot maintain its internal temperature owing to a freezing environment. As a result, the body temperature will decrease rapidly. The person will lose consciousness or faint when the body temperature falls below 35C. This study targets detecting climbers' hypothermia and transmitting their health status to the climber's group. It is difficult for mountain climbers to check their health and hypothermia symptoms for themselves and their climbing companions. To address this issue, we created a life jacket with an integrated hardware kit with a Peltier, temperature, and pulse sensor. LoRa Network is used to communicate with the climber's group. Alert messages are delivered to mountaineers via the Android app and suitable protocols, which helps save the climbers if any discrepancies occur. 2023 IEEE.
Neethu P.S.; Jesuwanth Sugesh R.G.; Praghash K.; Annalakshmi T.; Sushanth G.; Praveen D.
2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management, IC-RVITM 2023
Neethu P.S., Christ (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Karnataka, Bengaluru, India; Jesuwanth Sugesh R.G., Christ (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Karnataka, Bengaluru, India; Praghash K., Christ (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Karnataka, Bengaluru, India; Annalakshmi T., S A Engineering College, Department of Electronics and Communication Engineering, Chennai, India; Sushanth G., Christ (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Karnataka, Bengaluru, India; Praveen D., Christ (Deemed to Be University), School of Engineering and Technology, Department of Electronics and Communication Engineering, Karnataka, Bengaluru, India
Predicting a Rise in Employee Attrition Rates Through the Utilization of People Analytics
Artificial Intelligence; HR Analytics; Machine learning; Supervised learning
Modern organizations have a multitude of technological tools at their disposal to augment decision-making processes, with artificial intelligence (AI) standing out as a pivotal and extensively embraced technology. Its application spans various domains, including business strategies, organizational management, and human resources. There's a growing emphasis on the significance of talent capital within companies, and the rapid evolution of AI has significantly reshaped the business landscape. The integration of AI into HR functions has notably streamlined the analysis, prediction, and diagnosis of organizational issues, enabling more informed decision-making concerning employees. This study primarily aims to explore the factors influencing employee attrition. It seeks to pinpoint the key contributors to an employee's decision to quit an organization and develop a futuristic data driven model to forecast the possibility of an employee leaving the organization. The study involves training a model using an employee turnover dataset from IBM analytics, including a total of thirty-five features and approximately one thousand and five hundred samples. Post-training, the model's performance is assessed using classical metrics. The Gaussian Nae Bayes classifier emerged as the algorithm delivering the most accurate results for the specified dataset. It notably achieved the best recall (0.54) indicating its ability to correctly identify positive observations and maintained false negative of merely 4.5%. 2023 IEEE.
Proceedings of the 2023 12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023, pp. 349-355.
Arora S., School of Business & Management, Christ University, Ghaziabad, India; Kr Jha A., Institute of Technology and Science, Department of Management, Ghaziabad, India; Upadhyay S., Banasthali Vidyapeeth, Department of Computer Science, Rajasthan, India
Enhancing Security and Resource Optimization in IoT Applications with Blockchain Inclusion
Blockchain; Hybrid ABC-WOA; IoT; Resource Optimization; Security Enhancement; Time & Energy Consumption
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE.
2023 2nd International Conference on Futuristic Technologies, INCOFT 2023
https://doi.org/10.1109/INCOFT60753.2023.10425679 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187403520&doi=10.1109%2fINCOFT60753.2023.10425679&partnerID=40&md5=b31fdd490ff19cebbb419b8f8d2340a0
Malik R., School of Computer Applications, Lovely Professional University, Phagwara, India; Dua S., School of Computer Applications, Lovely Professional University, Phagwara, India; Shoran P., School of Science, Christ University, Noida, India; Nain A., School of Computer Applications, Lovely Professional University, Phagwara, India