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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Rathi, Snehal Khedkar, Vijayshri Ghule, Gauri Diwan, Niraj Atre, Kushagra Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Pune India Vishwakarma Institute of Information Technology Department of Entc Pune India

ISBN: (纸本)9798331504403

Student emotions significantly influence the learning process, yet traditional methods of monitoring emotional well-being are challenging. This research explores the potential of technology to understand and respond to student emotions in real-time. By analyzing facial expressions, body language, and physiological signals, the study aims to develop computer models capable of accurately predicting student emotions. This information can empower teachers to adapt their teaching methods to address the unique emotional needs of each student, creating a more engaging and effective learning environment. Machine learning and deep learning models, such as CNN and ResNet, are employed to analyze student data and predict emotions. The models are trained on a dataset of student facial expressions and body language to enhance accuracy and effectiveness. This research contributes to the development of innovative tools for improving student engagement and well-being in the classroom. By understanding and addressing student emotions, educators can create more personalized and supportive learning environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Machine Learning Algorithms for Emotion Classification 7

Comparative Analysis of Machine Learning Algorithms for Emot...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Deshmukh, Ashish Dhage, Aniket Gadapa, Ritika Butle, Sanika Yenkikar, Anuradha Sable, Nilesh P. Vishwakarma Institute of Information Technology Department of Cse - Artificial Intelligence Pune India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331527822

In this generation, social media platforms such as Twitter, Instagram and also e-commerce websites have become an integral part of our society. Social media is used for communication and it is also a platform where the users can express their thoughts and feelings. Also, users share their reviews on e-commerce websites. To analyze what exactly is the emotion of the user our machine learning models explores the sentiment analysis techniques for predicting emotions from the given text data. It follows a series of steps, starting with importing libraries, acquiring datasets, visualization, pre-processing, and model building. The data is used to train on a variety of models, including LSTM, Naive Bayes, Random Forest, Gradient Boosting, and SVM. The performance of each model is evaluated using metrics like accuracy, precision, recall, and F1-score. Comparative analysis is conducted to determine the most effective model for emotion prediction. Furthermore, the research investigates emotion prediction for huge datasets and provides a procedure for exporting trained models for future usage. It provides insights into the performance of various machine learning algorithms in sentiment analysis, hence contributing to the understanding and interpreting emotions from textual data on various platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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Investigation of DNA discontinuity for detecting tuberculosis

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems Science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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ANNDRA-IoT:A Deep Learning Approach for Optimal Resource Allocation in Internet of Things Environments

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computer Modeling in engineering & Sciences 2025年第3期142卷 3155-3179页

作者： Abdullah M.Alqahtani Kamran Ahmad Awan Abdulaziz Almaleh Osama Aletri Department of Electrical and Electronic Engineering College of Engineering and Computer ScienceJazan UniversityJazan45142Saudi Arabia Department of Information Technology The University of HaripurHaripur22620Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Department of Computing College of Engineering and ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia

Efficient resource management within Internet of Things(IoT)environments remains a pressing challenge due to the increasing number of devices and their diverse *** study introduces a neural network-based model that uses Long-Short-Term Memory(LSTM)to optimize resource allocation under dynam-ically changing *** to monitor the workload on individual IoT nodes,the model incorporates long-term data dependencies,enabling adaptive resource distribution in real *** training process utilizes Min-Max normalization and grid search for hyperparameter tuning,ensuring high resource utilization and consistent *** simulation results demonstrate the effectiveness of the proposed method,outperforming the state-of-the-art approaches,including Dynamic and Efficient Enhanced Load-Balancing(DEELB),Optimized Scheduling and Collaborative Active Resource-management(OSCAR),Convolutional Neural Network with Monarch Butterfly Optimization(CNN-MBO),and Autonomic Workload Prediction and Resource Allocation for Fog(AWPR-FOG).For example,in scenarios with low system utilization,the model achieved a resource utilization efficiency of 95%while maintaining a latency of just 15 ms,significantly exceeding the performance of comparative methods.

关键词： Internet of things resource optimization deep learning optimal resource allocation neural network efficiency

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A Novel Transfer Learning Method for Code Smell Detection on Heterogeneous Data: A Feasibility Study

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SN computer Science 2023年第6期4卷 1-21页

作者： Gupta, Ruchin Singh, Sandeep Kumar Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India Department of Information Technology Kiet Group of Institutions Delhi-NCR Ghaziabad India

Code smell detection has been primarily focused on homogeneous data. However, due to diverse sources of data, in a real-life scenario, the unseen target data on which code smell needs to be predicted may be heterogeneous in feature space representation from source data for which code smells are known. Also, the capability of a state-of-the-art technique of machine learning called “transfer learning” has not been well explored to transfer the knowledge of already known code smells from source data to predict code smells on unseen heterogeneous target data. This paper has examined the feasibility of transfer learning to predict code smells on unseen heterogeneous target data. The paper has proposed a novel method for detecting code smell on heterogeneous data using modified domain invariant transfer kernel learning (DITKL), one of the transfer learning techniques. The experiments were conducted using modified DITKL on six traditional machine learning models on long method and temporary field code smells. Results showed that modified DITKL on Naïve Bayes and the ID3 decision tree outperformed others for long method code smell, and modified DITKL on Multilayer Perceptron and Naïve Bayes performed well for temporary field code smell. The proposed method can be quite useful to detect code smells in unseen heterogeneous data when a tool or expert knowledge cannot be applied to detect a code smell due to characteristics of the unseen data. It can also help in establishing benchmark data for code smells. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Code smell Domain invariant transfer kernel learning Traditional machine learning Transfer learning

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua Science and technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Impact of Human Vulnerabilities on Cybersecurity

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computer Systems Science & engineering 2022年第3期40卷 1153-1166页

作者： Maher Alsharif Shailendra Mishra Mohammed AlShehri Department of Information Technology College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaah11952Saudi Arabia

Today,security is a major challenge linked with computer network companies that cannot defend against *** vulnerable factors increase security risks and cyber-attacks,including viruses,the internet,communications,and *** of Things(IoT)devices are more effective,and the number of devices connected to the internet is constantly increasing,and governments and businesses are also using these technologies to perform business activities ***,the increasing uses of technologies also increase risks,such as password attacks,social engineering,and phishing *** play a major role in the field of *** is observed that more than 39%of security risks are related to the human factor,and 95%of successful cyber-attacks are caused by human error,with most of them being insider *** major human factor issue in cybersecurity is a lack of user awareness of cyber *** study focuses on the human factor by surveying the vulnerabilities and reducing the risk by focusing on human nature and reacting to different *** study highlighted that most of the participants are not experienced with cybersecurity threats and how to protect their personal ***,the lack of awareness of the top three vulnerabilities related to the human factor in cybersecurity,such as phishing attacks,passwords,attacks,and social engineering,are major problems that need to be addressed and reduced through proper awareness and training.

关键词： Cybersecurity phishing attack password attack social engineering cybersecurity awareness security risk

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Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

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computer Systems Science & engineering 2023年第3期44卷 2129-2145页

作者： V.Thanikachalam M.G.Kavitha V.Sivamurugan Department of Information Technology Sri Sivasubramaniya Nadar College of EngineeringChennai603110India Department of Computer Science and Engineering University College of Engineering PtukottaiRajamadam614701India

In recent times,Internet of Things(IoT)and Deep Learning(DL)mod-els have revolutionized the diagnostic procedures of Diabetic Retinopathy(DR)in its early stages that can save the patient from vision *** the same time,the recent advancements made in Machine Learning(ML)and DL models help in developing computer Aided Diagnosis(CAD)models for DR recognition and *** this background,the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network(ODBN)model i.e.,NS-ODBN model for diagnosis of *** presented model involves Interval Neutrosophic Set(INS)technique to dis-tinguish the diseased areas in fundus *** addition,three feature extraction techniques such as histogram features,texture features,and wavelet features are used in this ***,Optimal Deep Belief Network(ODBN)model is utilized as a classification model for *** model involves Shuffled Shepherd Optimization(SSO)algorithm to regulate the hyperparameters of DBN technique in an optimal *** utilization of SSO algorithm in DBN model helps in increasing the detection performance of the model *** presented technique was experimentally evaluated using benchmark DR dataset and the results were validated under different evaluation *** resultant values infer that the proposed INS-ODBN technique is a promising candidate than other existing techniques.

关键词： Diabetic retinopathy machine learning internet of things deep belief network image segmentation

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