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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhanumathi, M. Arthi, B. Srm Institute of Science and Technology Department of Computer Science and Engineering India Department of Computer Science and Engineering India College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350321425

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic System are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats. © 2023 IEEE.

关键词： Optimization

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A Scrutiny of Machine Learning Methods for the Detection and Identification of Cyber Intrusion

A Scrutiny of Machine Learning Methods for the Detection and...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Eluri, Rama Krishna Valicharla, Karunakumar Divya, Modalavalasa Anusha, K.B. Narasaraopeta Engineering College Department Of Computer Science & Engineering Narasaraopet India Aditya Institute Of Technology And Management Department Of Computer Science & Engineering Tekkali India

ISBN: (纸本)9798350371567

This study investigates the interruption identification problem for organization safe havens;the main goal is to classify network behavior as normal or abnormal while minimizing misclassification. In this investigation, two effective information mining techniques are combined to identify network disruption. This methodology makes use of the two computations while avoiding their flaws, combining SVM, Decision Tree, and K Means together for effective information arrangement. This approach is implemented and surveyed using the standard benchmarking KDDCUP99 informational collecting. The experimental findings outperformed the other methods in terms of precision rate and run duration productivity, and this computation is suitable for detecting novel attack kinds. © 2024 IEEE.

关键词： Decision trees

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A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine

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Multimedia Tools and Applications 2024年第36期83卷 84121-84140页

作者： Thakur, Ujwala Prajapati, Amarjeet Vidyarthi, Ankit Department of Computer Science Engineering & amp Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida201309 India

The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In such situations, the smart sensors are utilized to capture the details remotely, share, and process intelligently using the computer-assisted machine. This paper introduces the new variant of the machine learning classifier, named Grid Search Based Nonlinear Multi-task Least Squares Twin Support Vector Machine (GMLT-SVM), to recognize human actions using sensory data. The proposed approach uses the grid search for hyperparameter tuning with a Nonlinear kernel for Multi-task classification in a twin support vector machine. This not only increases the efficacy of the model but also reduces the computation time for finding the optimal values of the hyper-parameters. The proposed approach when experimented with the sensory data of Human Action Recognition (HAR) gives an accuracy of 96.8%. The comparative analysis of the proposed method with existing other ML classifiers gives an increase in the accuracy of 94.2±2.6%. The experimental results suggest that the gained results are statistically significant enough to recognize human actions with sensory data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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In-Group Bias in Deep Learning-Based Face Recognition Models Due to Ethnicity and Age

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2023年第1期4卷 54-67页

作者： Nagpal, Shruti Singh, Maneet Singh, Richa Vatsa, Mayank Ratha, Nalini K. Ratha Indraprastha Institute of Information Technology Delhi Department of Computer Science and Engineering New Delhi110020 India Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Jodhpur342011 India University at Buffalo Department of Computer Science and Engineering BuffaloNY14260 United States

Humans are known to favor other individuals who exist in similar groups as them, exhibiting biased behavior, which is termed as in-group bias. The groups could be formed on the basis of ethnicity, age, or even a favorite sports team. Taking cues from aforementioned observation, we inspect if deep learning networks also mimic this human behavior, and are affected by in-group and out-group biases. In this first of its kind research, the behavior of face recognition models is evaluated to understand if similar to humans, models also encode group-specific features for face recognition, along with where bias is encoded in these models. Analysis has been performed for two use-cases of bias: age and ethnicity in face recognition models. Thorough experimental evaluation leads us to several insights: (i) deep learning models focus on different facial regions for different ethnic groups and age groups, and (ii) large variation in face verification performance is also observed across different sub-groups for both known and our own trained deep networks. Based on the observations, a novel bias index is presented for evaluating a trained model's level of bias. We believe that a better understanding of how deep learning models work and encode bias, along with the proposed bias index would enable researchers to address the challenge of bias in AI, and develop more robust and fairer algorithms for mitigating bias as well as developing fairer models. © 2020 IEEE.

关键词： Face recognition

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Public Decision-Making Analysis in Urban Heat Islands Based on Socioeconomic Modelling by Deep Learning Techniques

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 108-117页

作者： Prakash, S. Wilson Kiruthigha, R. Yadav, Ajay Kumar Kanna, S. K. Rajesh Shaik, Khasimbee Krishna, B. Murali Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Kattankulathur India Department of Information Technology SRM Institute of Science and Technology Chennai Ramapuram India Department of Computer Application United Institute of Management UP Prayagraj 211010 India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Urban heat island (UHI) effects, especially in highly urbanised areas, and greenhouse gas emissions from human activity are two elements that accelerate global climate change (GCC). Sustainable city planning and modification may benefit from the quantification of the surface UHI (SUHI) effect utilising land surface temperature (LST), local climate zones (LCZ), and deep learning methods like convolutional neural networks (CNN) and pix2pix. The findings show that socioeconomic variables account for 12–20% of variability in UHI intensity. In general, the contribution rate of urban economic scale is higher than that of population and industrial structure variables. The increase in urban economic expansion increased summertime heat discomfort. This research propose novel method in socioeconomic analysis based on urban heat pattern analysis with public healthcare system using decision-making with DL (deep learning) model. Input is collected as urban heat pattern dataset and processed for noise removal and normalisation. This data has been features are extracted and classified using convolutional fuzzy Q-Gaussian neural network and variational K-C-means regressive learning. The simulation analysis has been carried out based on training accuracy, average precision, recall, and F-measure. The proposed model attained average precision was 94%, recall was 90%, training accuracy was 97%, and F1-score was 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Decision-making Deep learning model Public healthcare system Socioeconomic analysis Urban heat pattern

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DARCVAA: A Deep Neural Networks Based Framework for Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis

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SN computer Science 2024年第5期5卷 1-21页

作者： Sinruwng, Ngamwal Yogita Pal, Vipin Verma, Anshul Department of Computer Science and Engineering National Institute of Technology Meghalaya Meghalaya Shillong 793003 India Department of Computer Science and Engineering National Institute of Technology Kurukshetra Haryana Kurukshetra 136119 India Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi New Delhi 110078 India Department of Computer Science Banaras Hindu University UP Varanasi 221005 India

In response to the global COronaVIrus Disease of 2019 (COVID-19) pandemic, widespread vaccination campaigns were initiated worldwide. To minimise Adverse Drug Reactions (ADRs) linked to the vaccines, extensive pre-clinical trials and post-marketing surveillance activities were undertaken in a traditional setting. However, there has been a limited exploration of social media data for this purpose. As social media data is composed of real-time user experiences on vaccines, its analysis is pivotal for understanding vaccine safety. This work proposes a framework named Detecting Adverse Reactions of COVID-19 Vaccines and Association Analysis (DARCVAA). It employs four Deep Neural Networks (DNN) based classification models to detect ADRs of COVID-19 vaccines from Reddit’s posts. Further, it uses the Apriori algorithm to extract associations between vaccines and ADRs to comprehensively understand their relationships. The statistical significance of the extracted associations has been evaluated in terms of support and confidence. The proposed framework has been applied to a dataset collected from Reddit’s platform from September 2020 to July 2021 and annotated with the help of a medical expert. The experimental results showed that the proposed framework has outperformed six state-of-the-art detection models, which include Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT), Vaccine Adverse Events-Mine (VAEM-Mine), Vaccine Adverse Events (VAE), Support Vector Machines (SVM) and Naıve Bayes (NB) in respect to precision, recall, F1-score and accuracy. The identified ADRs have been validated from the official surveillance reports, and the validation results have proven the efficacy of the proposed framework toward ADR detection and capturing possibly emerging ADRs. The analysis of detected ADRs in terms of frequent ADRs, ADRs unique to vaccines and genders and associations o

关键词： Adverse reactions of vaccines BioBERT COVID-19 Deep neural networks Social media

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An Improved Fuzzy Grounded Dynamic Package Priority Resolve and Line Management System in Wireless Sensor Network

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SN computer Science 2025年第2期6卷 1-12页

作者： Songare, Lokendra Singh Kumhar, Bhupchand Rajput, Ajeet Singh Shukla, Prashant Kumar Nigam, Ankita Gautam, Prakhar Ahluwalia, Ritu Department of Computer Science and Engineering Medicaps University MP Indore India Department of Computer Science & Engineering IES Institute of Technology and Management IES University MP Bhopal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L Deamed to be University Vaddeswaram Andhra Pradesh Guntur 522302 India Princeton Institute of Engineering and Technology for Women Telangana Hyderabad India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Department of CSBS Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line Management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DMP FDMP PPI QOS WSN

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An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

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computer Systems Science & engineering 2023年第4期45卷 149-166页

作者： P.Sherubha S.P.Sasirekha A.Dinesh Kumar Anguraj J.Vakula Rani Raju Anitha S.Phani Praveen R.Hariharan Krishnan Department of Information Technology Karpagam College of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationAndhra PradeshIndia Department of MCA CMR Institute of TechnologyBengaluruKarnatakaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyAndhra PradeshIndia Department of Computer Science and Engineering Residency CollegeChennaiIndia

The Cloud system shows its growing functionalities in various industrial *** safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill ***,Machine Learning(ML)approaches have been used for the construction of intellectual *** IDS are based on ML techniques either as unsupervised or *** supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack ***,the unsupervised model fails to provide a satisfactory ***,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good ***,the Naïve Bayes classifier is used for classification *** approach exposes the finest generalization ability to train the *** unlabelled data is also used for adoption towards data ***,redundant and noisy samples over the dataset are *** validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD *** experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and ***,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,*** compared to prevailing techniques,the anticipated approach also delivers promising outcomes.

关键词： Network intrusion detection system feature selection auto-encoder support vector machine(SVM) anomaly

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Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy

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Journal of Electrical Systems 2024年第1期20卷 66-75页

作者： Mall, Pawan Kumar Srivastava, Swapnita Patel, Mitul M. Kumar, Aniruddh Narayan, Vipul Kumar, Sanjay Singh, P.K. Singh, D.S. GL Bajaj Institute of Technology and Management India Department of Electronics and Communication Engineering Parul Institute of Engineering and Technology Parul University Vadodara India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Knowledge Park-2 Greater Noida India Galgotias University Uttar Pradesh Gautam Buddha Nagar India Rajkiya Engineering College Azamgarh India Computer Science and Engineering Department Madan Mohan Malaviya University Of Technology Uttar Pradesh Gorakhpur273010 India Computer Science and Engineering Department Madan Mohan Malaviya University of Technology Uttar Pradesh Gorakhpur273010 India

Predicting heart attacks stands as a significant concern contributing to global morbidity. Within clinical data analysis, cardiovascular disease emerges as a pivotal focus for forecasting, wherein Data Science and machine learning (ML) offer invaluable tools. These methodologies aid in predicting heart attacks by considering various risk factors Just like high blood pressure, increased cholesterol levels, irregular pulse rates, and diabetes, this research aims to enhance the accuracy of predicting heart disease through machine learning *** study introduces a MLdriven approach, termed ML-ELM, dedicated to forecasting heart attacks by analysing diverse risk factors. The proposed ML-ELM model is compared with alternative Utilizing machine learning techniques like Support Vector Machines, Logistic Regression, Naïve Bayes, and XGBoost is a key aspect of this exploration into different approaches for predictive modeling., is part of the research strategy. The dataset utilized for heart disease symptoms is sourced from the UCI ML Repository. The outcomes reveal that our proposed ML-ELM model has demonstrated superior predictive performance among the ML techniques tested. ML models show notable efficiency in identifying heart attack symptoms, particularly with boosting algorithms. Accuracy assessments were employed to gauge the predictive ability, Our suggested model demonstrated an outstanding accuracy rate of 96.77%. © JES 2024 on-line: ***

关键词： Support vector machines

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Segmentation of Skin Lesion Image Using FCM with Color Transformation Saliency 1

Segmentation of Skin Lesion Image Using FCM with Color Trans...

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1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Bavya, S. Saravana Kumar, V. Kavitha, M. Anantha SivaPrakasam, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science & Engineering Chennai India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India S A Engineering College Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science & Technology Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

In the majority of Western nations including America, Australia and Europe, skin cancer is badly-behaved. Skin concealing, inadequacy of Sun-lights, climate, age, and inherited are major risk factors. Early identification of skin cancer and proceed the treatment can avoid death. The abnormal skin region determines where the skin diseases had been found. Various sorts of skin ailments be existent currently. Melanoma is the sort of skin threat which has grows the greater part of mortality rate. The timely identification and intercession of melanoma include myriad changes of fix. Melanoma has been recognized by shape, size, concealing and outward of the skin injury. This paper demonstration about the traditional clustering technique in particular Fuzzy Clustering technique is acted in these skin lesion pictures. This strategy is segmenting this melanoma from these dermoscopic images effectively. The presentation of this strategy is assessed as far as pixel grouped and time complexity. © 2024 IEEE.

关键词： Feature extraction

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