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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Singh, Shubham Singh, Pawan Tanwar, Sudeep Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

ISBN: (纸本)9789819914784

Cloud computing has been developed as a front-line platform for consumer and commercial potential. Additionally, it enables users to access data and apps from any location. Businesses will use cloud services to borrow storage as well as other operations, which significantly lowers the cost of technology. In this aspect, resource allocation is more important to assure the efficiency of the cloud service. The primary goal of this study is to suggest a new resource allocation model that uses optimization to help maximize sales while minimizing cost. An improved k-means clustering with an optimal tuned centroid is used to cluster all workload (the jobs that have been allotted). Here, tasks are clustered as per the consideration of QoS (trust) and task execution time. Subsequent to this, the resource allocation is done optimally under the consideration of power utilization effectiveness (PUE), CPU usage, and execution time, respectively. For solving the optimization problem specified, this work proposes a new self-upgraded coot optimization (SUCO) algorithm. Finally, the proposed SUCO algorithm was evaluated over the other models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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ASocTweetPred: Mining and Prediction of Anti-social and Abusive Tweets for Anti-social Behavior Detection Using Selective Preferential Learning 13th

ASocTweetPred: Mining and Prediction of Anti-social and Abus...

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13th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

作者： Bhaveeasheshwar, E. Deepak, Gerard Mala, C. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru India Manipal Academy of Higher Education Manipal Udupi India

ISBN: (纸本)9783031274985

In the present times, owing to a large number of social interactions via Online Social Networking platforms. Because the content is not moderated on the World Wide Web, especially on the Social Web, due to lack of moderation, there are many derogatory, Anti-Social, and racist comments. Specifically, Twitter, YouTube, Instagram, Facebook comments, and similar social network platforms. In this paper, ASocTweetPred for Mining and Prediction of Anti-Social and Abusive Tweets for Anti-Social Behavior Detection has been put forth by encompassing Ontologies of different phases, Knowledge Graph, and the classification of the dataset using the LSTM to generate and yield matching ontology snippets by computing the Semantic Similarity using the Bose Einstein’s Index and the APMI measure. Subsequently, the matched ontology snippets and the formulated Knowledge Graph are used in order to extract features by preferential selection using Shannon’s Entropy and Horn’s Index with differential step deviation measures. A triadic hybrid model of the LSTM, Bagging with Random Forest and SVC, and XGBoost classifiers are made use for Anti-Social Tweets prediction. An overall Precision of 97.93%, Recall of 98.09%, Accuracy of 98.01%, F-Measure of 98.00%, and with the lowest FNR of 0.02 has been achieved by the proposed ASocTweetPred framework. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Machine Learning Classifiers for Social Media Bots Detection on Twitter using Explainable AI 2

Machine Learning Classifiers for Social Media Bots Detection...

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2nd International Conference on informatics, ICI 2023

作者： Gupta, Sarishty Sinha, Adwitiya Jaypee Institute of Information Technology Department of Computer Science and Engineering and Information Technology Noida India

ISBN: (纸本)9798350343830

Social networks have shaped modern society by revolutionizing how people connect and communicate with others around the world. Social media bots have become more prevalent as a result of social networks' phenomenal development and influence, nevertheless. These automated systems that are guided by algorithms or malicious actors can mimic human behavior and provide the impression that actual people are there. Growing concerns about the integrity of online debate, the dissemination of false information, and potential public opinion manipulation have been sparked by the use of social media bots. It is now necessary to create efficient procedures for identifying and thwarting social media threats in order to tackle this expanding danger. Such efforts use a variety of methods, including as examining profile metadata, observing behavioral patterns, and utilizing the strength of machine learning algorithms. In this paper, we propose an approach which uses multiple features of a user entity on Twitter to train a model for social media bot detection. We employed Twibot-22 dataset to conduct the experiment and compared the performance of our approach with established benchmark models in order to assess its efficacy. Remarkably, our Random Forest model outperformed the benchmarks, achieving an accuracy rate of 0.96. We utilized explainable AI technique to enhance the interpretability of random forest model. This study provides insights that are of immense value in refining the model and delving deeper into the distinguishing features of social bots and human users within the Twitter environment. © 2023 IEEE.

关键词： Lime

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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

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Journal of Cyber Security and Mobility 2023年第4期12卷 465-496页

作者： Shetty, Nisha P. Muniyal, Balachandra Priyanshu, Aman Das, Vedant Rishi Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem;however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy. © 2023 River Publishers.

关键词： Learning systems

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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection 1

Convolutional Neural Network Model based Deep Learning Appro...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Dhanalakshmi, R. Thenmozhi, M. Saxena, Swati Mahalingam, Hemalatha Vellore Institute of Technology School of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India KCG College of Technology Department of Computer Science and Engineering Chennai India King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798350336009

Osteoporosis is a bone ailment which takes place because of minimum bone physique, damaging of micro-structure of bone, more over an excessive vulnerability to breakage. The main fitness difficulty throughout the globe is Osteoporosis, particularly in aged people. It may create spinal or hip breakages which can result in morbidity and burden. Hence the diagnosis of Osteoporosis at early stages and forecasting the existence of the fracture is highly essential. However automated analysis and diagnosis of osteoporosis from virtual radiographs could be very difficult as they have little variations. The proposed approach in this work uses high-dimensional textured function representations calculated from radiography pictures to distinguish healthy from osteoporotic issues. CNN helps to identify osteoporosis using structural MRI measurements of bone with high accuracy © 2023 IEEE.

关键词： Bone

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Prediction of Student Performance in Learning Online Courses using Ensemble Learning Algorithms 4

Prediction of Student Performance in Learning Online Courses...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Pavithra, Jetti Sriharsha, V. Basha, P. Hussain Babu, T. Kishore Pace Institute Of Technology Sciences Department Of Computer Science & Engineering AP Ongole India Pace Institute of Technology Sciences Department of CSE AP Ongole India

ISBN: (纸本)9798350385793

Understanding and forecasting student performance has become essential for educators and administrators due to the widespread use of online learning platforms. This research study examines how well ensemble learning algorithms predict the performance of students enrolled in distance learning programs. Ensemble learning, which combines several base learners to increase generalization and prediction accuracy, presents a viable method for handling the challenging nature of student performance prediction problems. This research examines an ensemble learning method that analyses student data from online learning platforms using Hidden Markov Models (HMM) and XGBoost. The data gathered from multiple web sources was trained using the Decision Trees (DT) training technique. The comprehensive feature collection, comprising engagement metrics, demographic data, and past academic achievements, was obtained from student interactions through Ensemble feature extraction. The ensemble models use these features as inputs and are trained to predict student outcomes like retention, grades, and course completion. Through thorough testing and assessment of real-world datasets, it has been shown that ensemble learning successfully predicts student performance in various online courses and academic fields. When comparing the performance of ensemble models to conventional single-model methods, it is possible to see considerable gains in prediction resilience and accuracy. Lastly, the interpretability of ensemble models to learn more about the elements influencing how well students succeed in virtual learning environments. Several functional patterns and connections are identified that can guide intervention tactics, instructional design, and personalized learning experiences by examining feature importance and model explanations. © 2024 IEEE.

关键词： Federated learning

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Secure Management of Digital Academic Certificates Using Blockchain technology 3rd

Secure Management of Digital Academic Certificates Using Blo...

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3rd International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, MIND 2021

作者： Biswas, Anindya Kumar Dasgupta, Mou Ray, Sangram Department of Computer Application National Institute of Technology Raipur Raipur492010 India Department of Computer Science and Engineering National Institute of Technology Sikkim Ravangla Sikkim737139 India

ISBN: (纸本)9789811958670

The education sector is striving/needed to bring innovation in its different activities and the blockchain technology (BT), which is proposed for Bitcoin cryptocurrency, has promising property in securely decentralizing many applications including the ledger for banking systems. In this paper, we employ blockchain in academics for secure storage and easy retrieval/online verification of the hard-earned certificates to be used in diverse areas namely jobs, higher educations, forgery-detection, etc. Note that the academic certificates are indispensable for everyone’s lives, and it is the time to keep such important property in non-destructible manner but with easy accessibility to all concerned. The basic idea is that an educational institution while publishing the results of examinations, in addition to the distribution of certificates/marksheets to the students must incorporate the same in blockchain as smart certificates and make them available in peer-to-peer (P2P) network. This avoids the maintenance of registers for results kept by colleges, universities, education sectors, etc.;however, it supports for proofs of degrees claimed by students and online quick verification for any issue including fake certificate submission. An institution based on the departments, disciplines and various programs run creates multiple blockchains corresponding to the registers maintained, though the first block called the genesis block contained institute’s identity, would be the common for all. For conceptualization, a prototype design for management of blockchain-based smart certificates with various uses is presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Video-based Hand Gesture Recognition using Random Forest for Sign Language Interpretation

Video-based Hand Gesture Recognition using Random Forest for...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Himasree, J. Jeevitha, P.L. Deekshitha, K. Kolisetty, Aashrita Naveen, Soumyalatha REVA University School of Computer Science & Information Technology Bangalore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science & Engineering Manipal India

ISBN: (纸本)9798350361537

Effective communication is essential for all individuals, yet it can be particularly challenging for the deaf community, as sign language is their major means of communication. We are researching how to create a reliable and effective system that can understand sign language motions correctly without the help of a human, realizing the importance of this *** this research work we have chosen to construct our own dataset rather than depending on pre-existing ones that may be accessed online because we understand how important authenticity and relevance are. This method gives us more control over the quality of the data and guarantees that the dataset closely matches our study goals. In addition, our system can be readily expanded and recognizes a wide range of hand motions and signs, including the alphabet. We can support a broad variety of sign language expressions thanks to this versatility, which improves inclusivity and usability. Random Forest is used to encode temporal variations among motions in sign language. The system's overall accuracy and reliability are increased thanks to this methodology, which enables it to detect minute variations in hand gestures and movements. Using the Keras and OpenCV libraries in conjunction with the Python programming language, we build a strong foundation for sign language recognition. In addition to streamlining the development process, these technological tools open the system to a larger developer and research community. In alignment with the research, the proposed work achieved an accuracy of 97% with overall precision, recall and F1 score of 99%. © 2024 IEEE.

关键词： Gesture recognition

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Early Detection of Fire in EV Battery Using Machine Learning Approach 2

Early Detection of Fire in EV Battery Using Machine Learning...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2023

作者： Kaur, Ishpreet Singh, Mukesh Kasana, Singara Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India Thapar Institute of Engineering and Technology Electrical and Instrumentation Engineering Department Patiala India

ISBN: (纸本)9798350300802

In recent years, Electric Vehicles (EVs) have revolutionized the automobile industry. Nowadays, EVs are the preferred means of transportation for people because they are easy to drive, convenient, and make less noise. EVs use Lithium-Ion Batteries (LIB) to power the vehicle. However, in past years, there have been multiple instances of EVs catching fire and becoming one of the most urgent problems in EVs. The leading causes of such mishaps include faulty Battery Management System (BMS), no proper venting mechanism for overheated cells to release heat, poor quality cells, and short-circuiting. Many researchers proposed fire handling systems that consider sensor values, such as temperature, voltage, etc. while some focus on battery health management. However, there is a need for a detection mechanism to detect fire in advance to save people's life and property from damage. Some researchers have proposed fire detection strategies considering only a few sensor readings that cannot detect the extent of a fire early and inform users about the situation. Therefore, in the proposed work, an approach is designed considering both environmental and battery data using multi-sensors for developing an intelligent early fire detection system. The proposed approach first performs exploratory data analysis on real dataset to get an insight into data for fault diagnosis, leading to early thermal runaway. Further, Machine Learning (ML) and ensemble methods are used to check the potentiality of the fire or detect fire in advance. Finally, the system is evaluated concerning accuracy, recall, precision, and Fl-Score. © 2023 IEEE.

关键词： Battery Management System Electric Vehicles Fire Detection Lithium-Ion Battery Machine Learning

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