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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Arunarani, Ar Vijayagopal, V. Kartheesan, L. Dwivedi, Vijay Kumar Rajesh Kumar, A. Nishant, Neerav School of Computing College of Engineering Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Chennai India Dr. M.G.R. Educational and Research Institute Faculty of Computer Applications Tamilnadu Chennai India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Vishwavidyalaya Engineering College Department of Mathematics Chhattisgarh Surguja India N.S.N. College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Karur India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331540364

many services have made the transition to the cloud because of the increased reliability and efficiency of cloud computing. Due to their capacity to ease service access while also securing communications in the public network, the three-factor mutual Authentication and Key Agreement (mAKA) protocols are seeing increased use in multi-server designs. A lack of formal security proof or high communication and computation costs makes the limited three-factor mAKA protocols that are currently available susceptible to a wide range of attacks. The proposed approach consists of three phases, which are preprocessing, feature selection, and model training. Text scheduling and normalization make up preprocessing. In feature selection, the PSO is an optimization method that uses a population-based approach to find optimal solutions. An ANN-SVm was employed throughout the model's training phase. With an average accuracy of 93.30%, the proposed technique surpasses ANN and SVm. © 2024 IEEE.

关键词： Cloud security

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A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Preetha, V. Sathishkumar, P. Janani, m. manohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well. © 202

关键词： Sentiment analysis

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monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced marine and Polar Dynamics

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTm) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from mODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTm-RNN model achieves high predictive accuracy, with root mean square error (RmSE) values as low as 0.08 m for ice thickness and a mean absolute error (mAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTm (RmSE 0.12 m, mAE 0.09 m) and CNN-LSTm hybrid (RmSE 0.10 m, mAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTm-RNN machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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Intrusion detection based on phishing detection with machine learning

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measurement: Sensors 2024年 31卷

作者： Jayaraj, R. Pushpalatha, A. Sangeetha, K. Kamaleshwar, T. Udhaya Shree, S. Damodaran, Deepa Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur TN Chennai India M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology TN Coimbatore India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Dr. Rangarajan Dr.Sagunthala R&D Institute of Science and Technology TN Chennai India Department of Computer Science and Engineering Alpha College of Engineering and Technology Puducherry India VITBS Vellore Institute of Technology Chennai Campus TN India

machine learning technique which uses artificial neural networks to learn representations. Phishing is a form of fraud in which the attacker tries to learn credential information from the websites. Web phishing is to steal sensitive information such as usernames, passwords and credit card details by way of impersonating a authorized entity. The Hybrid Ensemble Feature Selection is a new feature selection method for machine learning-based phishing detection systems (HEFS). The first step of HEFS involves using a novel Cumulative Distribution Function gradient (CDF-g) algorithm to generate primary feature subsets, which are then fed into a data perturbation ensemble to generate secondary feature subsets. We present the results of our approach and compare them to a few previous studies, with the paper focusing primarily on phishing urls for detecting the unauthorised one by using phishing detection method. © 2023

关键词： Intrusion detection

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Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

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measurement: Sensors 2024年 33卷

作者： Yamini, B. G, Pradeep D, Kalaiyarasi m, Jayaprakash G, Janani G S, Uthayakumar India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology R.M.K. Engineering College India Department of Information Technology R.M.D Engineering College India Department of Electronics and Communication Engineering St.Joseph's Institute of Technology India

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data. © 2024

关键词： Internet of things

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Cloud Computing: Innovations and Impacts on Global Data Infrastructure

Cloud Computing: Innovations and Impacts on Global Data Infr...

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2025 IEEE International Conference on Computational, Communication and Information technology, ICCCIT 2025

作者： Banumathi, S. Sivajothi, E. Indumathi, G.S. Kefyalew, Enku Yosef Sivakumar, J. Priya, V. Banu Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India St Joseph's College of Engineering OMR Department of MBA Chennai India Technical and Vocational Training Institute Department of Electrical and Electronics Technology Addis Ababa Ethiopia St.Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Thiruvallur District India

ISBN: (纸本)9798331512965

Cloud computing has transformed global data infrastructure with unparalleled scale, flexibility, and accessibility. This study examines how cloud computing innovations have changed data storage, processing, and management worldwide. By examining the progression of cloud services from simple storage and computing to sophisticated platforms and SaaS, we examine how they have transformed corporate operations, economic growth, and societal development. Virtualization, containerization, serverless architectures, and edge computing are the main cloud computing advancements, which we investigate through a literature study and case studies. Data security, privacy, and regulatory frameworks are also considered while adopting the cloud. Cloud computing promotes digital transformation in banking, healthcare, education, and government, according to study. The industry's sustainability and green computing efforts and data centers' environmental impacts are also covered. We found that cloud computing continues to spur innovation by enabling new business models, global connectivity, and data-driven application growth. Cloud technologies' integration with AI, IoT, and 5G networks is likely to change the global data infrastructure, opening new research and development options. This report covers the cloud computing landscape, its advancements, and its potential to shape global data management and digital ecosystems. © 2025 IEEE.

关键词： Cloud platforms

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Enhancing Remote Sensing Image Classification using Arithmetic Optimization Algorithm with Deep Learning Approach 2

Enhancing Remote Sensing Image Classification using Arithmet...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Shanthi, R. Balasubramaniam, D. Venkatesh, R. Sankari, C. manimaran, V. Basha, Shaik Hedayath Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chengalpattu Tamilnadu Kattankulathur603203 India Department of Biomedical Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India Ramco Institute of Technology Rajapalayam India Chennai Institute of Technology India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamilnadu Erode638060 India Department of ECE R.M.K. College of Engineering and Technology India

ISBN: (纸本)9798350383867

The classification of Remote sensing image (RSI)is a vital task in field of geospatial analysis where aerial or satellite images are classified into predefined classes or land cover sorts. It plays a main part in diverse fields namely agriculture, disaster management, environmental surveillance, urban planning, and natural resource management. With the machine learning (mL) and deep learning (DL) models, these images examined and categorized depending on their spatial, spectral and temporal features. The exact detection of remote sensing images simplifies decision-making procedures, aiding governments, environmental agencies, and companies to make knowledgeable decisions and policies. It authorizes experts to observe variations in lands, discover land use trends, and evaluate environmental effects and making it a vital device in addressing present difficult challenges connected to weather change, urban development, and natural resource protection. This study presents an enhanced remote sensing image classification utilizing arithmetic optimization algorithm with DL (ERSC-AODL) Approach. The ERSC-AODL technique concentrates on the accurate recognition of the RSI into dissimilar classes. It begins with the use of an EfficientNet feature extractor, which derives complex spectral and spatial data, and acts as a base of proper representation of the images. moreover, the recognition process takes place by a bidirectional long short-term memory (BiLSTm). Finally, the hyperparameter optimization procedure is performed by the ERSC-AODL technique. To portray the proficient classification outputs of the ERSC-AODL technique, a range of experiments were utilized. The investigational outputs stated that the ERSC-AODL technique attains improved accomplishment over other models. © 2024 IEEE.

关键词： Decision making

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Revolutionizing Cardiovascular Attack Prediction: A Comprehensive machine Learning Approach for Accurate and Timely Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. machine Learning (mL) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing mL techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector machine (SVm), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing User Data Privacy and Confidentiality in Cloud Storage Systems Through Advanced Obfuscrypt Encryption methods for Enhanced Security and Efficient Data Protection

Optimizing User Data Privacy and Confidentiality in Cloud St...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Ramesh, P.S. Sudha, I. Jagannathan, J. Pandiarajan, Rajkumar Ponmalar, A. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India Kalasalingam Academy Of Research and Education School of Computing Department of Information and Technology Krishnankoil Virudhunagar India R.M.K. Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350388602

With a steady increase in the adoption of cloud storage systems, the client's data are at risk of being leaked. There are several methods of encrypting data which are quite useful but they lack some of the features of today's sophisticated threats. The methodology used in this research propose an encryption framework known as Obfuscrypt to improve the issues of data privacy as well as confidentiality of data stored in cloud storage systems. Obfuscrypt uses conventional encryption strategies incorporated with obfuscation and post-quantum cryptographic algorithms to provide a complex security plan immune to classic and quantum approaches. Some of the proposed other innovations include the decentralised key management with multi-factor authentication and RBAC-ABE as the fine-grained access control techniques. These features make it possible for only the authorized personnel to be in a position to be in a position of decrypting and retrieving the stored data thereby minimizing the possibility of the data being accessed by unauthorized people. The work also considers the issue of performance efficiency of the cloud storage systems. Similar to other symmetric encryption programs, Obfuscrypt uses light encryption algorithms and brings in to play the hardware acceleration so that it may infuse lesser computational overhead in the process of encryption and decryption. As can be clearly seen from the results, the actual work proves that the proposed framework not only offers a higher level of security but does not affect the system performance to a great extent. Also, the study addresses the post-quantum cryptographic algorithm for making the system resistant to an attack that can be posed by quantum computing systems. With the help of these improved algorithms, Obfuscrypt guarantees the permanent data security in the world of constantly emerging cyber threats. © 2024 IEEE.

关键词： Data obfuscation

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Securing multi-Tenant Cloud Environments with Fully Homomorphically Encrypted Secure multiparty Computation

Securing Multi-Tenant Cloud Environments with Fully Homomorp...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Jagannathan, J. R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India

ISBN: (纸本)9798350388602

The aim of this work is to examine the application of FHE-SmPC as a viable security model for protecting the tenant isolation in multi-tenant cloud model. In multi-tenant environments, where various customers use the same cloud system, controlling the data leakage and protecting privacy is a major issue. Actual safeguards fail to meet the needs of data protection during processing, therefore, the introduction of highly elaborate encryption processes must happen. This research work focuses on the use of FHE technique and SmPC such that data can undergo processing while the plaintext remains unknown to any party. A kind of approach is based on the acquisition of synthetic and real data sets, system-level architecture, and cryptographic algorithms using open-source libraries with cloud service providers. The results of such implementation indicate that despite the fact that the FHE-SmPC system has a considerable computational overhead in comparison with classical schemes it is very secure and protected against different types of cryptographic attacks. The problem of scalability is still an open issue, but the complex is quite efficient with various data types that is why it can be used in various cloud applications. This paper offers a pertinent solution for security of data that is housed in multitenant environment and will go along way in creating fertile ground for research and innovation in secure clouding. © 2024 IEEE.

关键词： Cloud security

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