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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Muthumanickam, K. Geetha, M. Mahesh, P.C. Senthil Vedhashree, S.K. Selvarathi, C. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Kongunadu College of Engineering and Technology Department of Information Technology Tholurpatti Tamil Nadu Trichy621215 India M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639113 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641021 India

ISBN: (纸本)9798331509675

Today social media has fundamentally altered global communication and information exchange. However, as these platforms have become more widely used, so has cyber-hatred which is a significant problem that has caught academics' attention. This article has been addressed by a variety of methods from deep learning and machine learning, including recurrent neural networks, convolutional neural networks, logistic regression, and Naive Bayes. These techniques distinguish between various classes using mathematical concepts. However, sentiment-oriented data offers an accurate picture of how the public reads communications online, accurate classification necessitates a more "critical thinking"perspective. Two machine learning classifiers were employed in this study, one of which was built on a review of the literature to look at efficient classification techniques. Four online hatred datasets using logistic regression and multinomial naive Bayes. To better grasp the text in the datasets, the classifier results were enhanced by using XG-Boost algorithm. © 2025 IEEE.

关键词： Adversarial machine learning

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An Uncertainty-Aware Deep Learning-Based Model for COVID-19 Diagnosis 3

An Uncertainty-Aware Deep Learning-Based Model for COVID-19 ...

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3rd IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2023

作者： Reddy, L Chandra Sekhar Jayakarthik, R. Kiran, Ajmeera Sharma, Nipun Sharma, Swati Reddy, Pundru Chandra Shaker Hyderabad India Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamil Nadu Chennai India MLR Institute of Technology Dundigal Computer Science and Engineering Telangana Hyderabad India Presidency University Electronics and Communication Engineering Karnataka Bangalore India School of Computer Science Engineering and Information Science Presidency University Karnataka Bangalore India

ISBN: (纸本)9798350317022

The application of deep-learning(DL) to the processing of medical images has been highly successful. A few groundbreaking DL-based works have achieved substantial progress in computerized screening of COVID-19 using chest X-ray( CXR) pictures in the wake of the current epidemic scenario caused by SARS-CoV-2. While DL models have shown promise, they currently lack a crucial component for use in medical image analysis: the ability to communicate uncertainty in the strategies prediction. Therefore, in this paper, we create a design for automatically detecting COVID19 from CXR images using an uncertainty-aware convolutional-neural-network(UACNN) and estimate the uncertainty in its predictions. In the suggested method, an EfficientNetB3 model is tuned using CXR pictures, and then the model is used in conjunction with Monte Carlo (MC) dropout. The posterior predictive distribution has been obtained after M forward passes of inference using MC dropout. The anticipation and design uncertainty are then determined by calculating the mean and entropy of the generated anticipative distribution. The COVID19CXr, XR-pictures, and Kaggle CXR datasets are used to test the suggested approach. Multi-class classification on the COVID19CXr dataset was completed with an accuracy of 99.81% using the proposed UACNN approach. For the purpose of diagnosing COVID-19 cases from CXR pictures, our suggested methodology demonstrates its superiority over the existing methodologies. © 2023 IEEE.

关键词： CNN COVID19 CXR deep-learning transfer-learning uncertainty-quantification

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Audiovisual speech recognition based on a deep convolutional neural network

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Data Science and Management 2024年第1期7卷 25-34页

作者： Shashidhar Rudregowda Sudarshan Patilkulkarni Vinayakumar Ravi Gururaj H.L. Moez Krichen Department of Electronics and Communication Engineering JSS Science and Technology UniversityMysuru570006India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobar34754Saudi Arabia Department of Information Technology Manipal Institute of Technology BengaluruManipal Academy of Higher EducationManipal560064India Department of Information Technology Faculty of Computer Science and Information Technology(FCSIT)Al-Baha UniversityAlaqiq65779-7738Saudi Arabia ReDCAD Laboratory University of SfaxSfax3038Tunisia

Audiovisual speech recognition is an emerging research *** is the recognition of what someone is saying using visual information,primarily lip *** this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual *** features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural *** feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural ***,integration was performed using a deep convolutional *** audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 *** training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 *** integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

关键词： Audiovisual speech recognition,Custom dataset 1D Convolution neural network(CNN) Deep CNN(DCNN) Long short-term memory(LSTM) Lipreading Dlib Mel-frequency cepstral coefficient(MFCC)

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Scoring Impressions and Associations for Improved Concept Map Excavating from Dominion Text Demonstration

Scoring Impressions and Associations for Improved Concept Ma...

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2023 IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2023

作者： Mohan Gowda, V. Sanjeev Kumar, A.N. Kumar, Dhanesh Hemalatha, K.L. Chaithra, N.K. Gitam School of Technology Gitam University Department of Computer Science & Engineering India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

A concept map is a graphical tool used to organize and represent knowledge or information visually. It consists of a collection of concepts or ideas that are linked together by relationships or connections to form a structured and hierarchical representation of a subject or topic. A concept map can be used to show the relationships between ideas, identify patterns, and clarify complex information. It is commonly used in education, research, and problem-solving to help individuals better understand and visualize information. The graph's entropy serves as a useful indicator of how accurately it represents the document. This study explains how to construct a concept map from the document's sentences by using a tree-based convolution neural network to extract key concepts. The use of latent relations based on a shared word embedding representation of concepts enables the removal of duplication and the efficient organization of concepts. This leads to a more precise, concise, and unambiguous concept map. The paper also describes the modifications made to the concept map and the additional data incorporated into it. By utilizing the latent semantic structure and hierarchical linkage of ideas and by adding significant information to the concepts themselves, the information content of the concept map is improved. © 2023 IEEE.

关键词： Entropy

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Forecasting of Groundwater Quality Utilizing an Efficient K-Means with Principal Component Analysis Approach 2

Forecasting of Groundwater Quality Utilizing an Efficient K-...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Adnan, Myasar Mundher Manjula, B.M. Mishra, Antim Dev Ganti, Swathi Bala Dhandayuthapani, V. The Islamic University of Babylon Babylon Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering School of Engineering and Technology Sushant College Gurgaon India Department of Nano Technology Andhra University Visakhapatnam India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman

ISBN: (纸本)9798331504960

Groundwater stands as a pivotal freshwater resource in numerous regions worldwide, but the burgeoning population and diverse economic activities have adversely impacted its quality. However, the K-means algorithm is sensitive to the initial placement of centroids, where outliers can significantly influence the position of centroids resulting in suboptimal clusters. To address this, it is recommended to remove or clip outliers before applying K-means scales well to large datasets making it suitable for handling substantial amounts of data. In this proposed method, attributes are first calculated using Principal Component Analysis (PCA) determines the weight of each attribute based on eigenvalues. The PCA, by determining attribute weights and Rapid Centroid Estimation (RCE), by establishing the initial cluster centre, contribute to the overall effectiveness of the method. The classification step utilizes a Linear Support Vector Machine (SVM), suitable for linear hyper line data when the dataset can be classified into two classes using a single straight line. The results obtained showcase the superiority of the proposed method, achieving a Root Mean Squared Error (RMSE) of 1.23, R-squared (R2) of 0.90, Mean Absolute Deviation (MAD) of 0.28 and E of 0.97 on the Indian groundwater dataset. These outcomes affirm better detection and classification performance compared to other existing methods, such as Random Forest (RF), Decision Tree (DT) and K-Nearest Neighbour (KNN). © 2024 IEEE.

关键词： Mean square error

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Advanced Machine Learning Techniques for Enhancing Data Security in Cloud Computing Systems 5

Advanced Machine Learning Techniques for Enhancing Data Secu...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

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Throughput Enhancement in Wireless Sensor Network by Novel Genetic Algorithm-Fuzzy Method (NGA-F) 3rd

Throughput Enhancement in Wireless Sensor Network by Novel G...

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3rd International Conference on VLSI, communication and Signal processing, VCAS 2020

作者： Prakash, N. Rajalakshmi, M. Nedunchezhian, R. Department of Electronics and Communication Engineering KIT-Kalaignarkarunanidhi Institute of Technology Coimbatore India Department of Computer Science and Engineering and Information Technology Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789811627606

Demodulating the unsafe hubs along with reducing the vitality squander in sensor hubs can drag out the life expectancy of remote sensor systems. By doing the examination, adaptive heuristic search algorithms and an adaptive neuro fuzzy inference system (ANFIS) framework were utilized to lessen the vitality misuse of sensors. Biased trust assessment was connected to look for unsafe hubs in the system for drawing out the life expectancy of the sensor network. Low-vitality versatile grouping chain that has some importance strategies were utilized for breaking down the outcomes. It was found that looking for destructive hubs having genetic algorithm (GA)—Adaptive Network-based Fuzzy Inference System (ANFIS) utilizing subjective trust assessment altogether expanded the life expectancy of WSNs. For assessment of the proposed technique that was utilized by the means of vitality of all sensors against the round, information that are obtained in base station, least vitality versus adjusts and number sensors that are alive sensors versus rounds. Likewise, the manuscript has the proposed strategy along with the results and Dual Fuzzy Logic-Cluster Protocol (DFLCP), Low-Energy Adaptive Clustering Hierarchy (LEACH), Centralized Low-Energy Adaptive Clustering Hierarchy (LEACH-C), Enhanced Distributed Energy Efficient Clustering (EDEEC), Hybrid Energy Efficient Distributed clustering (HEED). As results the proposed Novel Genetic algorithm with Fuzzy (NGA-F) strategy has high life time than different techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy inference

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Improved Secure Identification-Based Multilevel Structure of Data Sharing in Cloud Environments

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computer Systems Science & engineering 2022年第11期43卷 785-801页

作者： Saraswathi Shunmuganathan Sridharan Kannan T.V.Madhusudhana Rao K.Ambika T.Jayasankar Department of Computer Science and Engineering SSN College of EngineeringChennaiTamil Nadu603110India Department of Computer Science and Engineering J.K.K.Munirajah College of TechnologyErodeTamil Nadu638506India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalliTamil Nadu620024India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India

The Cloud Computing Environment(CCE)developed for using the dynamic cloud is the ability of software and services likely to grow with any *** has transformed the methodology for storing the enterprise data,accessing the data,and Data Sharing(DS).Big data frame a constant way of uploading and sharing the cloud data in a hierarchical architecture with different kinds of separate privileges to access the *** the requirement of vast volumes of storage area in the CCEs,capturing a secured data access framework is an important *** paper proposes an Improved Secure Identification-based Multilevel Structure of Data Sharing(ISIMSDS)to hold the DS of big data in *** complex file partitioning technique is proposed to verify the access privilege context for sharing data in complex *** access control Encryption Method(EM)is used to improve the *** Complexity is measured to increase the authentication *** active attack is protected using this ISIMSDS *** proposed ISIMSDS method assists in diminishing the Complexity whenever the user’s population is increasing *** security analysis proves that the proposed ISIMSDS methodology is more secure against the chosen-PlainText(PT)attack and provides more efficient computation and storage space than the related *** performance of the proposed ISIMSDS methodology provides more efficiency in communication costs such as encryption,decryption,and retrieval of the data.

关键词： Data sharing cloud environments big data chosen-plaintext attack security

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A Reconfigurable Reflectarray Antenna at X-band 7

A Reconfigurable Reflectarray Antenna at X-band

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Du, Lin Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

In this paper, an electronically reconfigurable reflectarray antenna (RRA) that operates in X-band is developed and simulated. Two P-I-N diodes are integrated in each element to obtain 1-bit phase compensation. The proposed element offers a stable angular response and polarization rotation in X-band. The element is applied to a 19×19 RRA for achieving beam scanning. The simulated result of the proposed RRA shows that the proposed RRA can achievea beam scanning ranging from -30° to 30° and a peak gain of 18.12 dBi. © 2024 IEEE.

关键词： Reflection

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A Design of High-Gain, Polarimetric Conversion Reflection Filter Array 7

A Design of High-Gain, Polarimetric Conversion Reflection Fi...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Rao, Liwei Feng, Botao Ding, Xiao Gu, Wenzhe Deng, Li Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Huizhou University School of Computer Science and Engineering Huizhou China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

An "I"-shaped polarization conversion unit was developed in this study, capable of functioning at a frequency of 12 GHz. The design involved rotating metal patches on the unit by 90 degrees, resulting in an alternative unit with the same reflection coefficient and a phase difference of 180°. The final step involved incorporating choke rings around the "I"-shape to provide filtering functionality. Based on this, a high-gain filtering reflective array consisting of 20×20 proposed antenna unit was constructed. At 12 GHz, the attained gain was 24.7 dBi, and the radiation patterns for both H-plane and V-plane are demonstrated to be stable enough. © 2024 IEEE.

关键词： Polarization

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