Malicious Traffic Classification in WSN using Deep Learning Approaches
data routing; deep learning; LSTM; malicious transmission; neural network; Traffic data; transmission; WSN
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE.
Gupta C.L.P.; Rajassekharan D.; Sharma D.K.; Elangovan M.; Myilsamy V.; Upreti K.
2023 International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023, pp. 426-431.
Institute of Electrical and Electronics Engineers Inc.
Gupta C.L.P., Bansal Institute of Engineering and Technology, Dept. of Computer Science and Engineering, Lucknow, India; Rajassekharan D., Peninsula College, Faculty of Computer Science, Peninsula, Malaysia; Sharma D.K., Jaypee University of Engineering and Technology, Department of Mathematics, Madhya Pradesh, Guna, 473226, India; Elangovan M., K. S. Rangasamy College of Technology, Department of Artificial Intelligence and Data Science, Tamil Nadu, Tiruchengode, 637215, India; Myilsamy V., V.S.B College of Engineering Technical Campus, Department of Electrical and Electronics Engineering, Tamil Nadu, Coimbatore, 642109, India; Upreti K., CHRIST(Deemed to Be University), Department of Computer Science, Delhi-NCR, Ghaziabad, India
Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network
Convolutional Neural Network; Database Management; Deep Learning; Face Recognition; Generative Adversarial Networks
In today's digital age, manual attendance tracking is plagued by inefficiency and the potential for inaccuracies, often leading to proxy attendance. The main aim of this research work is to manage and monitor the student's attendance by using face recognition technology. This proposed model is mainly categorized four major modules. First module is database creation. Second module is face detection. Then third module is face recognition and final module is automatic attendance updating process. Student images are compiled to create a comprehensive database, ensuring inclusivity across the class roster. The system utilizes the face recognition library, which relies on deep learning based algorithms for face detection and recognition during testing. This face recognition part Convolutional Neural Network algorithm is used. The system matches detected faces with the known database and marks attendance, ensuring a streamlined and accurate attendance tracking process. This innovative approach has the potential to revolutionize attendance management in educational settings, offering a contactless and efficient solution while mitigating proxy attendance concerns. The proposed model is to compare the accuracy level of face recognition. 2023 IEEE.
Joseph A.; Nair A.; Koshy A.B.; Nallaballi M.; Jayapandian N.
3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023
Joseph A., (Deemed to Be University), Department of Cse Christ, India; Nair A., (Deemed to Be University), Department of Cse Christ, India; Koshy A.B., (Deemed to Be University), Department of Cse Christ, India; Nallaballi M., School of Engineering and Technology Christ (Deemed to Be University), India; Jayapandian N., School of Engineering and Technology Christ (Deemed to Be University), India
Vehicular Propagation Velocity Forecasting Using Open CV
Computer Vision; Convolutional Neural Network; Machine Learning; Vehicle Speed Detection
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE.
Das U.; Sharma V.; Das M.; Mishra S.; Iwendi C.; Osamor J.
2023 4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023
Das U., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Das M., Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., Kalinga Institute of Industrial Technology, Bhubaneswar, 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
Machine Learning Based Recession Prediction Analysis Using Gross Domestic Product (GDP)
Covid-19; Economical Statistics; Gross Domestic Product; Machine Learning; Random Forest; Recession
This research article aims to explore the prediction and analysis of recessions, with a particular focus on Gross Domestic Product (GDP). The study examines the impact of recessions on different countries, namely India, USA, Germany, China, and Bangladesh, while also considering the influence of the COVID-19 pandemic on these nations in relation to the recessionary effects. Furthermore, the study lists many machine learning techniques that could be used to anticipate recessions. This research mainly focuses on predicting recession using different machine learning models. The research not only provides an in-depth analysis of the recessionary impacts on different economies but also serves as a foundation for future implementation of these algorithms for accurate recession prediction and proactive economic decision-making. This research study mainly focuses on machine learning algorithms like Random Forest, Support Vector Machines and Regression Model. The GDP prediction comparison is taking last twenty years data. This is mainly compared before and after COVID-19 situation. 2023 IEEE.
7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 492-498.
Haritha T.H., CHRIST (Deemed to Be University), Department of CSE, India; Joby A., CHRIST (Deemed to Be University), Department of CSE, India; Javapandian N., CHRIST (Deemed to Be University), Department of CSE, India
Identification of Cyberbullying and Finding Target User's Intention on Public Forums
bag of words; cyberbullying; feature extraction; sentiment analysis; SVM
Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study. 2023 IEEE.
Krishnasamy L.; Kiruthika M.; Manjunatha Swamy C.; Vigneshwaran T.; Sharon Roji Priya C.; Vijay Anand D.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 2152-2156.
Krishnasamy L., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Kiruthika M., School of Computing, Department of Computational Intelligence, SRM Institute of Science and Technology, Tamilnadu, Kattankulathur, India; Manjunatha Swamy C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vigneshwaran T., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Sharon Roji Priya C., School of Engineering and Technology, Department of CSE, Christ University, Bengaluru, India; Vijay Anand D., Kongu Engineering College, Department of Information Technology, Erode, India
Predictive Analytics for Stock Market Trends using Machine Learning
Data Engineering; Data Preprocessing; Decision Trees; Deep Learning; Feature Selection; Financial Data; Linear Regression; Machine Learning; Machine Learning Algorithms; Random Forests; Recurrent Neural Networks (RNNs); Stock Market Trends
Navigating the intricacies of stock market trends demands a novel approach capable of deciphering the web of financial data and market sentiment. This research embarks on a transformative journey into the realm of machine learning, where we harness the power of data to forecast stock market trends with increased precision and accuracy. Commencing with an exploration of stock market dynamics and the inherent limitations of traditional forecasting techniques, this paper takes a bold step into the future by embracing the potential of machine learning. The study begins with an in-depth analysis of data preprocessing, unraveling the complexity of feature selection and engineering, setting the stage for a data-driven odyssey. As our exploration progresses, we dive into the deployment of diverse machine learning algorithms, including linear regression, decision trees, random forests, and the formidable deep learning models such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These algorithms act as our guiding lights, revealing intricate patterns concealed within historical stock price data. Our journey reaches new heights as we recognize the significance of augmenting predictive models with external data sources. Incorporating elements like news sentiment analysis and macroeconomic indicators enriches our understanding of the market landscape, enhancing the predictive capabilities of our models. We also delve into the crucial aspects of model evaluation, guarding against overfitting, and selecting appropriate performance metrics to ensure robust and reliable predictions. The research reaches its zenith with a meticulous analysis of real-world case studies, providing a comparative perspective between machine learning models and traditional forecasting methods. The results underscore the remarkable potential of machine learning in predicting stock market trends more accurately. 2023 IEEE.
Manasa N.; Praveenraj D.W.; Lakshmi S.R.
https://doi.org/10.1109/ICCAKM58659.2023.10449528 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187556499&doi=10.1109%2fICCAKM58659.2023.10449528&partnerID=40&md5=171d658a35aeb60e8ca851d7aaae7a84
Manasa N., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Praveenraj D.W., School of Business and Management, Christ (Deemed to Be University), Bangalore, India; Lakshmi S.R., School of Business and Management, Christ (Deemed to Be University), Bangalore, India
Impact of Expert Academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network
Bidirectional Long Short-Term Memory (LSTM); Convolutional Neural Network (CNN); Information Gain (IG)
Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by Research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method. 2023 IEEE.
Shukla T.D.; Acharjee P.B.; Chethan C.; Thulasimani T.; Sindhu M.; Sharma S.
7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, pp. 895-900.
https://doi.org/10.1109/ICECA58529.2023.10395690 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186121150&doi=10.1109%2fICECA58529.2023.10395690&partnerID=40&md5=1dd07530ce8c5e424dd1b87fc22ed83e
Shukla T.D., University Institute of Teachers Training & Research, Chandigarh University, Mohali, India; Acharjee P.B., CHRIST University, Pune, India; Chethan C., Sri Venkateshwara College of Engineering, Department of Information Science and Engineering, Bengaluru, India; Thulasimani T., Bannari Amman Institute of Technology, Department of Mathematics, Tamilnadu, Sathyamangalam, India; Sindhu M., Prince Shri Venkateshwara Padmavathy Engineering College, Department of Computer Science and Engineering, Chennai, India; Sharma S., Madhav Institute of Technology and Science, Department of IT, Madhya Pradesh, Gwalior, India
A Comprehensive Review on Image Restoration Methods due to Salt and Pepper Noise
Convolutional Neural Network; Digital image; linear and non-linear methods; noise; Peak Signal to Noise Ratio; visualized information
Digital images are well-use in various fields like satellite communication, mobile communication, medical and security. Visualized information helps the people to understand the things easily by seen. Improper capturing, age of camera lens, imperfect storage and transmission leads to introduce noise in the image. Gaussian noise, salt and pepper/impulse noise and speckle noise may affect the original image due to aforementioned reasons. Out of these, impulse noise/salt and pepper noise is one of the major types, degrades the image with black and white spots it results loss of required information. Hence, restoration of ground- truth image from such type of noisy image is a challenging task to provide quality and clarity visuals to users. Several linear and non-linear methods have been proposed by researchers since more than four decades. Nonlinear methods based on; median filtering approach; adaptive median filter approach; median filter with switching condition; and median filter with rank order type; are proposed from early 1980s onwards. All of these operated directly on pixels in spatial domain. Hence, they are very easy to implement and most of them are not that much robust at middle and higher noise density circumstances. Further, various researchers have been implemented linear methods such as wavelet transform methods like SWT and DWT. Majority of these are works well upto 50% noise density conditions and very few works well on higher and multiple noise density conditions also. To overcome these problems CNNs based methods have been developed tremendously by various researchers from last decade and these methods require huge database to train the network model. Most of these, achieved good accuracy rates at higher and multiple noise conditions. Hence, here a detailed review report is presented on impulse noise removal methods with their Peak Signal to Noise Ratio (PSNR). 2023 IEEE.
Kumar G.P.; Arputharaj V.J.; Kumar P.R.; Kumar D.V.; Satyanarayana B.V.V.; Budumuru P.R.
2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023 - Proceedings, pp. 562-567.
Kumar G.P., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Arputharaj V.J., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Kumar P.R., Department of ECE, Sri Vishnu Engineering College for Women, Bhimavaram, India; Kumar D.V., Freshman Engineering, Department Lakireddy, Balireddy College of Engineering, Mylavaram, India; Satyanarayana B.V.V., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India; Budumuru P.R., Department of ECE, Vishnu Institute of Technology, Bhimavaram, India
Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
algorithmic bias; artificial intelligence; disease outbreak prediction; public health
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE.
Singhal M.; Mishra S.; Sharma V.; Anand K.; Alkhayyat A.
2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2023, pp. 1020-1025.
https://doi.org/10.1109/UPCON59197.2023.10434492 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187352329&doi=10.1109%2fUPCON59197.2023.10434492&partnerID=40&md5=6f6d4d2fb4eedfe0a8f8ecdd463e04b6
Singhal M., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Sharma V., Christ (Deemed to Be University), Computer Science Department, Delhi NCR, India; Anand K., Kalinga Institute of Industrial Technology, Deemed to Be University, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
A Voting Enabled Predictive Approach for Hate Speech Detection
CountVectorizer; Hate speech detection; logistic regression; machine learning; Naive Bayes classifier; Random Forest classifier
In today's digital environment, hate speech, which is defined as disparaging and discriminating communication based on personal characteristics, presents a big difficulty. Hate crimes and the rising amount of such content on social media platforms are two examples of how it is having an impact. Large volumes of textual data require manual analysis and categorization, which is tedious and subject to prejudice. Machine learning (ML) technologies have the ability to automate hate speech identification with increased objectivity and accuracy in order to overcome these constraints. This article intends to give a comparative analysis of various ML models for the identification of hate speech. The proliferation of such content online and its negative repercussions on people and society are explored, as is the necessity for automated hate speech recognition. This paper intends to support the creation of efficient hate speech detection systems by performing a comparative analysis of ML models. Random forest records the best performance with higher accuracy and low response delay period for hate speech detection. The results will help enhance automated text classification algorithms and, in the end, promote a safer and more welcoming online environment by illuminating the benefits and drawbacks of various approaches. 2023 IEEE.
Panda P.; Mishra S.; Sharma V.; Alkhayyat A.
Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2023, pp. 450-454.
Panda P., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Mishra S., School of Computer Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, India; Sharma V., CHRIST (Deemed to Be University), Computer Science Department, Delhi NCR, India; Alkhayyat A., College of Technical Engineering, The Islamic University, Najaf, Iraq
AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges
Autonomous car; Explainable artificial intelligence; Intelligent transport system
With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. 2023 IEEE.
Sinha K.; Misra B.; Chakraborty S.; Dey N.
Conference Proceedings - 2023 IEEE Silchar Subsection Conference, SILCON 2023
Sinha K., Christ (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Misra B., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Chakraborty S., Techno International New Town, Department of Computer Science Engineering, Kolkata, India; Dey N., Techno International New Town, Department of Computer Science Engineering, Kolkata, India
Prediction of Rainfall Using Seasonal Auto Regressive Integrated Moving Average and Transductive Long Short-Term Model
Accuracy rate; Rainfall prediction; Seasonal Auto Regressive Integrated Moving Average; Time Series Analysis; Transductive Long Short-Term Model
One of the most crucial parts of the practical application in recent years has been the analysis of time series data for forecasting. Because of the extreme climate variations, it is now harder than ever to estimate rainfall accurately. It is possible to forecast rainfall using a number of time series models that uncover hidden patterns in past meteorological data. Choosing the right Time Series Analysis Models for predicting is a challenging task. This study suggests using a Seasonal Auto Regressive Integrated Moving Average (SARIMA) to forecast values that are similar to historical values that exhibit seasonal patterns. Twelve years of historical weather data for the city of Lahore (from 2005 to 2017) and Blora Regency are taken into account for the prediction. The dataset underwent pre-processing operations like cleaning and normalisation before to the classification procedure. For classification, Transductive Long Short-Term Model (TLSTM) is employed which has learned the dependency values where the memory blocks are recurring and capable of learning long-term dependencies on this model. Further, TLSTM's goal is to increase accuracy close to the test point, where test points are selected as a validation group. The performance of the models has been assessed based on accuracy (99%), precision (98%), recall (96%) and fl-score (98%). Proposed SARIMA model showed optimistic results when compared to existing models. 2023 IEEE.
Phadke R.; Mohamad Ramadan G.; Archana Reddy R.; Kumar Pani A.; Al-Jawahry H.M.
IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
https://doi.org/10.1109/AIKIIE60097.2023.10390346 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187257517&doi=10.1109%2fAIKIIE60097.2023.10390346&partnerID=40&md5=eefe494d87dd726dbda70f9945426acb
Phadke R., Nitte Meenakshi Institute of Technology, Department of Electronics and Communication Engineering, Bangalore, India; Mohamad Ramadan G., College of Mlt Ahl Al Bayt University, Karbala, Iraq; Archana Reddy R., School of Computer Science and Artificial Intelligence Sr University, Warangal, India; Kumar Pani A., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Al-Jawahry H.M., The Islamic University, Najaf, Iraq
Augmented Reality Based Medical Education
3D learning; 3D model creation; Augmented reality; virtual reality
The education in medical field requires both theoretical knowledge and practical knowledge. It is important for medical student to acquire effective practical skills. Since the students apply the theoretical knowledge in practical manner in human body. Human body is very volatile, gentle, and difficult system. If a student apply trial in the humans for practical knowledge, there may cause the human error which leads to death of the person. To avoid this, the proposed system 'Augmented Reality Based Medical Education' is useful. Augmented reality makes the learning process more interactive and interesting. It can reproduce specific circumstances that assist students to rehearse with virtual objects that look like the human body and organ. Like traditional learning, it does not require real patients. By this way, augmented reality prevents risk of human life. Medical education with augmented reality extensively provides real time experiences. It has low risks and also affordable. When any human error occurs, there is no human loss. So the human life can be prevented by the system. The proposed system is developed using tools like Unity which is the complete platform for the developing our application, Vuforia-developer portal, a tool to create image target and Blender which is used to create 3D objects. 2023 IEEE.
Sathyamoorthy M.; Dhanaraj R.K.; Vanitha C.N.; Krishnasamy L.
2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023
https://doi.org/10.1109/ICCEBS58601.2023.10449124 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189136834&doi=10.1109%2fICCEBS58601.2023.10449124&partnerID=40&md5=27671fa15aff8df479bfd1171f8254fd
Sathyamoorthy M., Kpr Institute of Engineering and Technology, Department of Information Technology, Coimbatore, India; Dhanaraj R.K., Symbiosis Institue of Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India; Vanitha C.N., Kongu Engineering College, Department of Computer Science and Engineering, Erode, India; Krishnasamy L., School of Engineering and Technology, Christ (Deemed to Be) University, Department of Computer Science and Engineering, Kengeri Campus, Bengaluru, 560074, India
5G Technology Empowering Wireless Technology
Base Stations; Sink; Wireless Communication; WSN (Wireless Sensor Networks)
Wireless Communication is the means of transferring data from one point to another without the use of any wired means. With reference to wireless communication, wireless sensor Networks (WSN) have also developed in recent times. It can be referred as an infrastructure-less system of wireless devices which can gather and exchange information with the help of a wireless link. The information which is gathered is sent respectively to the base stations and sinks for further developments. Recently, the 5G generation network, the latest Wireless Communication Network operates at a higher frequency range than its predecessor. In this paper, a detailed analysis on the 5G generation cellular network, which is expected to be a key instrument of wireless technologies in the near future is outlined. Also a comparative analysis of different kinds of networks in context to wireless scenario is discussed. It was found that 5G provides the best outcome in terms of high speed and network spectrum bandwidth. 2023 IEEE.
Basuri S.; Parida A.; Mishra S.; Sharma V.; Al-Khasawneh M.A.; Alkhayyat A.
2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, pp. 878-883.
https://doi.org/10.1109/ICAICCIT60255.2023.10465779 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189898567&doi=10.1109%2fICAICCIT60255.2023.10465779&partnerID=40&md5=17fd6df943266adcd556b21c3a665015
Basuri S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Parida A., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Mishra S., Kalinga Institute of Industrial Technology, Deemed to Be University, Bhubaneswar, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi NCR, India; Al-Khasawneh M.A., Skyline University College, University City Sharjah, School of Computing, Sharjah, 1797, United Arab Emirates; Alkhayyat A., The Islamic University, College of Technical Engineering, Najaf, Iraq
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
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.
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.
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