An Efficient Approach for Obstacle Avoidance and Navigation in Robots
Bayesian algorithms; DQN algorithm; dynamic policy; extended dynamic policy; physical environment; real-world transfer.; reinforcement learning; reward signal; robot obstacle avoidance; simulation; static algorithms
Reinforcement learning has emerged as a prominent technique for enhancing robot obstacle avoidance capabilities in recent years. This research provides a comprehensive overview of reinforcement learning methods, focusing on Bayesian, static, dynamic policy, Deep Q-Learning (DQN) and extended dynamic policy algorithms. In the context of robot obstacle avoidance, these algorithms enable an agent to interact with its physical environment, learns effective operating strategies, and optimize actions to maximize a reward signal. The environment typically consists of a physical space that the robot must navigate without encountering obstacles. The reward signal serves as an objective measure of the robot's performance towards accomplishing specific goals, such as reaching designated positions or completing tasks. Furthermore, successful obstacle avoidance strategies acquired in simulation environments can be seamlessly transferred to real-world scenarios. The promising results achieved thus far indicate the potential of reinforcement learning as a powerful tool for enhancing robot obstacle avoidance. This research concludes with insights into the future prospects of reward learning, high-lighting its ongoing importance in the development of intelligent robotics systems. The proposed algorithm DQN outperforms well among all the other algorithms with an accuracy of 81%, Through this research, we aim to provide valuable insights and directions for further advancements in the field of robot obstacle avoidance using reinforcement learning techniques. 2023 IEEE.
Phani Shanmukh M.; Natarajan B.; Kannan C.; Tamilselvi M.; Vigneshwaran T.; Husain S.S.
International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023
Institute of Electrical and Electronics Engineers Inc.
Phani Shanmukh M., Amrita Vishwa Vidyapeetham, Amrita School of Computing, Department of Computer Science and Engineering (AIE), Chennai, India; Natarajan B., Vit University, School of Computer Science and Engineering, Chennai, India; Kannan C., Cmr Institute of Technology, Department of Computer Science and Engineering, Hyderabad, India; Tamilselvi M., Roever Engineering, Department of Computer Science and Engineering, Perambalur, India; Vigneshwaran T., Christ (Deemed to Be University), School of Engineering and Technology, Department of Cse, Bangalore, India; Husain S.S., K Ramakrishnan College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Tiruchirapalli, India
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
Bird Swarm Optimization; Cloud Computing; Firefly Algorithm; Load Balancing; Makespan
In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE.
Prasanna Kumar K.R.; Gm S.; Yamsani N.; Kiran Kumar T.M.; Pani A.K.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
Prasanna Kumar K.R., Msrit, Department of Cse, Bangalore, India; Gm S., Msrit, Department of Ai&ds, Bangalore, India; Yamsani N., School of Computer Science and Artificial Intelligence, Sr University, Warangal, India; Kiran Kumar T.M., Siddaganga Institute of Technology, Department of Cse, Tumkur, India; Pani A.K., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India
TSM: A Cloud Computing Task Scheduling Model
cloud resource; makespan; Min-Min; Task Scheduling; virtualization
Cloud offers online-based runtime computing services through virtualized resources, ensuring scalability and efficient resource utilization on demand. Resource allocation in the dynamic cloud environment poses challenges for providers due to fluctuating user demand and resource availability. Cloud service providers must dynamically and economically allocate substantial resources among dispersed users worldwide. Users, in turn, expect reliable and cost-effective computing services, requiring the establishment of Service Level Agreements (SLAs). Resource distribution uncertainty arises in view of the dynamicity of the cloud, where VMs, memory capacity requirement, processing power, and networking are allocated to user applications using virtualization technology. Resource allocation strategies must address issues such as insufficient provisioning, scarcity, competition, resources fragmentation. CPU scheduler plays a crucial role in task completion, by selecting job from queue considering specific requirements. The Task Scheduling Model (TSM) algorithm improves scheduling by considering expected execution time, standard deviation, and resource completion time, aiming to address resource imbalances and task waiting times. The research discusses previous work, presents experimental findings, describes the experimental setup and results, and concludes with future research directions. 2023 IEEE.
Kamalam G.K.; Shah N.H.; Krishnasamy L.; Manjunatha Swamy C.; Pathak L.K.; Vanitha P.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 1575-1580.
Kamalam G.K., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India; Shah N.H., College of Computer Science & Information Technology, Department of Computer Science, Jazan University, Jazan, Saudi Arabia; Krishnasamy L., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Manjunatha Swamy C., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Pathak L.K., School of Engineering and Technology, Department of CSE, Christ University, Karnataka, Bengaluru, India; Vanitha P., Kongu Engineering College, Department of Information Technology, Tamil Nadu, India
Machine Learning for Smart School Selection and Admissions
Admissions; Artificial Intelligence; Data-driven Decision Making; Education Technology; Educational Data Analysis; Machine Learning; Parental Decision-Making; Recommendation System; School Selection; Smart School
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.
Praveenraj D.D.W.; Victor M.; Subramani K.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
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
Sustainable Climatic Metrics Determination with Ensemble Predictive Analytics
Climate change; Linear regression; Machine learning; Random forest; Sustainability
Sustainable features are dependent on vital climatic elements that has a prominent impact on the retention of sustainability provided its metrics are in desired domain. Regression analysis and ensemble learning models are some of the predictive analytics methods which were used to detect the association of every feature on sustainable criteria. Weather samples from Delhi during 1970-2020 is used in the research which considers features like humidity, pollutant level, temperature etc which are gathered from several authenticated sites like pollution management unit of India. After analyzing several elements affecting weather endurability, it is noticed that pollutant level and temperature exhibit the highest significance recording 30% and 44% respectively. Also the R-square metric of 86% and 82% was observed with implementation of analytics models. The major conclusion recorded that random forest outperformed regression model and it established the importance of predictive analytics in predicting sustainability results. The research validated the relevance of climatic tracking for regulating sustainability. 2023 IEEE.
Pattanaik A.; Sharma V.; Kunj K.; Mishra S.; Iwendi C.; Osamor J.
https://doi.org/10.1109/ICCAKM58659.2023.10449578 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187553292&doi=10.1109%2fICCAKM58659.2023.10449578&partnerID=40&md5=26a71c24c5e842aa6df092d1ec7e5f76
Pattanaik A., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Kunj K., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Mishra S., Kalinga Institute of Industrial Technology, (Deemed to Be University), India; Iwendi C., School of Creative Technologies, University of Bolton, United Kingdom; Osamor J., Glasgow Caledonian University, Department of Cybersecurity and Networks, Glasgow, G4 0BA, United Kingdom
An Efficient Wireless Sensor Network based Intrusion Detection System
IDS; machine learning; responsive mechanism; Spectral clustering; WSN
Wireless Sensor Networks (WSNs) are widely used in diverse applications due to their cost-effectiveness and versatility. However, they face substantial difficulties because of their innate resource constraints and susceptibility to security attacks. A possible method to improve the security of WSNs is clustering-based intrusion detection and responding mechanisms. An in-depth analysis of the clustering-based intrusion detection and response method for WSNs is presented in this study. The suggested method efficiently uses data mining and machine learning techniques to identify unusual behaviour and probable intrusions. The system effectively analyses data inside clusters by grouping Sensor Nodes (SN) into clusters, allowing it to differentiate between legitimate patterns and insecure activity. The network may respond promptly to identified breaches and react to the responsive mechanism, which reduces their impact and protects network integrity. The proposed Mathematically Modified Gene Populated Spectral Clustering Based Intrusion Detection System and Responsive Mechanism (MMMMGPSC-IDS-RM) is compared with existing state-of-art techniques, and MMMMGPSC-IDS-RM outperforms with the highest detection rate of 96%. 2023 IEEE.
Rajassekharan D.; Ramachandran V.; Sharma D.K.; Palanisamy T.; Myilsam Y.V.; Poonia R.C.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 444-449.
https://doi.org/10.1109/ICCSAI59793.2023.10421537 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186706661&doi=10.1109%2fICCSAI59793.2023.10421537&partnerID=40&md5=6b0b997ed0ac9b6deb049ea0ee9232aa
Rajassekharan D., Peninsula College, Faculty of Computer Science, Malaysia; Ramachandran V., Gitam School of Computing, Gitam Deemed to Be 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; Palanisamy T., Nandha Engineering College, Department of Computer Science and Engineering, Tamil Nadu, Erode, India; Myilsam Y.V., V.S.B College of Engineering, Department of Electrical and Electronics Engineering, Technical Campus, Tamil Nadu, Coimbatore, 642109, India; Poonia R.C., Christ (Deemed to Be University), Department of Computer Science, Delhi-NCR, Uttar Pradesh, Ghaziabad, 201003, India
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.
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.
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