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An efficient Key Agreement and Authentication Scheme (KAAS) with enhanced security control for IIoT systems

International Journal of Information Technology (Singapore) 2023年第3期15卷 1221-1230页

作者： Srikanth, G. Umarani Geetha, R. Prabhu, S. Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering S.A. Engineering College Chennai India

Internet of Things is one of the prevalent and inevitable trends of the current era where data is found to be scattered among sensing devices. This paper addresses about preserving the privacy and security of the data that is distributed among the sensors collected by the users in Industrial Internet of Things infrastructure domain. This work adopts a dynamic authentication key agreement strategy which permits authenticated users to access the data which is distributed among various IoT sensing devices. In the proposed work an efficient Key Agreement and Authentication Scheme is designed to ensure the establishment of secure data communication. The model called Real-or-Random and tool named Automated Validation of Internet Security Protocols are deployed to ensure the secured transmission of data collected by innumerous sensors deployed across the network. Scaling of the network is done by addition of new nodes followed by pre-deployment phase and restoration of the transmission route is done effectively if any intrusion is found. The performance of the scheme is validated by measuring the parameters such as throughput, residual energy, packet dropping rate and delay analysis and it is found that the proposed scheme is superior compared with existing schemes. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Authentication Industrial Internet of Things (IIoT) Security Wireless sensor networks (WSNs)

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Mood Melody Matchmaker System Using Deep Learning Model 9

Mood Melody Matchmaker System Using Deep Learning Model

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Mishra, Susmita Pradhan, Chittaranjan Jananee, V. Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India School of Computer Engineering KIlT Deemed To Be University Bhubaneswar India Rajalakshmi Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

Accessing a wide variety of music is made simple by modern music services. Many users rely on recommendation algorithms to select the ideal song for any given circumstance. Due to its frequent use for relaxation, mood regulation, stress, and illness management, as well as support for mental and physical activities, music has a profound influence on individuals. Music therapy employs a range of therapeutic environments and methods to enhance overall well-being. The article suggests a mood melody matching related to the listener's feelings. Machine learning and deep learning methods are employed to create a recommendation system designed to assist individuals in choosing music suitable for various life circumstances. Efficiently recognizing the ideal track for the user's current mood, the system delivers songs tailored to their emotional state, eliminating the need for users to spend time searching or seeking out music themselves. © 2024 IEEE.

关键词： Python

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Bernstein polynomials and Bezier curves: a novel modeling approach to secure ECG data transmission

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1043-1053页

作者： Rajagopal, Prahalad Premnath, Pooja Arumugam, Chamundeswari Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

In the digital era, protecting sensitive medical data, including Electrocardiogram (ECG) signals, is of utmost importance. Maintaining the confidentiality, integrity, and authenticity of ECG signals while safeguarding their privacy is a critical challenge. This paper proposes a novel approach to safeguard ECG waveforms by utilizing polynomial modeling techniques followed by encryption. Polynomial functions, specifically Bernstein and Bezier-Bernstein polynomials, are employed for accurate approximation of the ECG signal, accounting for noise, interference, and channel distortions. The coefficients obtained from polynomial modeling are encrypted using the standard AES and Fernet algorithms. Decryption allows authorized access to the ECG signals, facilitating methodical waveform reconstruction. The proposed methodology’s performance is evaluated using various metrics to assess its effectiveness in protecting sensitive medical data. Metrics include Root Mean Square Error, Peak Signal to Noise Ratio, Correlation Coefficient, and Structural Similarity Index. The proposed method effectively safeguards ECG waveforms by combining polynomial modeling and encryption techniques. The approach offers a robust solution to protect sensitive medical data in the digital realm. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bernstein polynomials Bezier-Bernstein polynomials Electrocardiogram (ECG) Signal decryption Signal encryption

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Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

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computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 IEEE.

关键词： k-nearest neighbors

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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Developing a Secure Framework Using Feature Selection and Attack Detection Technique

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computers, Materials & Continua 2023年第2期74卷 4183-4201页

作者： Mahima Dahiya Nitin Nitin Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221USA

Intrusion detection is critical to guaranteeing the safety of the data in the *** though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion *** challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,*** to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection *** data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as ***,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack *** on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the ***,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced *** selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter ***,the selected features are trained and tested for detecting attacks using *** Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the

关键词： Cyber security data mining intrusion detection system(DataMIDS) marginal likelihood fisher information matrix(MLFIM) absolute median deviation based robust scalar(AMD-RS) functional perturbation(FP) inverse chi square based flamingo search optimization(ICS-FSO) hyperparameter tuned threshold based decision tree(HpTT-DT) Xavier normal distribution based relief(XavND-relief) and Bengio Nesterov momentum-based tuned generative adversarial network(BNM-tGAN)

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RNN based EPON dynamic bandwidth allocation algorithm for complex network

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Journal of Optical Communications 2024年第3期45卷 555-562页

作者： Yu, Jinxia Fu, Yanyan Xiao, Fan Jia, Huijuan Qin, Panke Zhao, Zongqu You, Junru Liu, Feiyang Han, Shangya Wang, Jiawei College of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China College of Computer Science and Technology Jilin University Changchun130012 China

In the development of ethernet passive optical networks (EPONs), quality of service (QoS) support and fairness per optical network unit (ONU) are crucial issues. However, making an elaborate analysis of the existing prediction-based bandwidth allocation algorithm, light load penalty, low prediction precision are pointed out. We present an improved dynamic bandwidth pre-allocation algorithm (R-DBA), which employs recurrent neural network (RNN) to predict the high-priority service traffic in EPON. And we introduce mixed integer linear programming (MILP) for optimally building DBA algorithm. This algorithm achieves the prediction of the high-priority service traffic by RNN during the waiting time and supports bandwidth pre-allocation, thus ensuring the fairness of the bandwidth allocation. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Complex networks

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UniVibe - college Social Network 1

UniVibe - College Social Network

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Nikunj Goel, Prakarti Lal, Sandeep Kumar, Arun Computer Science and Engineering I.T.S Engineering College Ghaziabad India Computer Science and Engineering I.T.S Engineering College Delhi India Computer Science and Engineering I.T.S Engineering College Greater Noida India

ISBN: (纸本)9798350356816

The UniVibe project represents a groundbreaking endeavor in the realm of student interaction within the university community. This dynamic and inclusive platform, designed as a web application, aims to revolutionize the way students connect, collaborate, and engage within the academic environment. At its core, UniVibe addresses the challenges associated with traditional communication barriers, such as batch divisions, academic year separations, and limited avenues for spontaneous interactions. The project embraces a strategic fusion of cutting-edge technologies, including *** for a hybrid rendering approach, Websockets for real-time communication, and WebRTC for multimedia sharing capabilities. The user-centric design of UniVibe places a strong emphasis on creating a seamless and responsive interface. The front-end architecture adopts a component-based structure, promoting code modularity and reusability. The hybrid rendering capabilities of *** contribute to optimized performance, combining server-side rendering (SSR) and static site generation (SSG) for swift initial loads and real-time content updates. UniVibe transcends conventional social circles, creating an environment where users can engage in real-time discussions, collaborate on projects, and share multimedia content effortlessly. WebRTC integration introduces multimedia sharing capabilities, allowing users to engage in face-to-face interactions and collaborative endeavors in real time. This feature enriches the depth of communication within the platform, providing a multifaceted experience that goes beyond text-based interactions. The back-end architecture, ensures scalability and efficient handling of concurrent connections. Aiven for database, accommodates the dynamic nature of user-generated content, providing flexibility in data storage. As for Security measures, JSON Web Tokens (JWT) and Clerk is being used for authentication and authorization, safeguard user information and ensure the confid

关键词： Reusability

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