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Advanced Machine Learning Approaches for Improving Traffic Flow Predictions in Smart Transportation Systems 1

8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Kanakaprabha, S. Ganeshkumar, G. Sureshkumar, A. Udhayakumar, T. Nisha, M. Poornaprakash, P. Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India Department of Information Technology Rathinam Technical Campus Anna University Coimbatore India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Anna University Coimbatore India

ISBN: (数字)9783031511677

ISBN: (纸本)9783031511660

Traffic flow extrapolation is a vital aspect of intellectual transportation systems, as it facilitates the smooth and efficient management of traffic. However, traditional traffic flow prediction methods have several limitations, including a lack of accuracy and the inability to handle large and complex data. In this study, advanced machine learning techniques are suggested for enhancing the precision and efficiency of traffic flow predictions in intelligent transportation systems. We collect and preprocess a large and diverse dataset of historical traffic flow data, and use techniques such as feature engineering, model selection, model evaluation, model tuning, and model ensemble to develop a robust-based traffic flow prediction system. The system suggested in this research integrates KNN, Random Forest, and ARIMA machine learning algorithms to forecast traffic flow considering various factors including weather, road conditions, and traffic volume. The outcomes show that the projected system significantly enhances the accuracy of traffic flow predictions compared to traditional methods and can handle a wide range of traffic scenarios and conditions. This research demonstrates the potential of advanced machine learning approaches for improving traffic flow predictions in smart transportation systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Large datasets

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Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

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computers, Materials & Continua 2021年第6期67卷 3161-3176页

作者： Osama M.Abu Zaid Medhat A.Tawfeek Saad Alanazi Department of Computer Science Faculty of Computers and InformationKafrelsheikh UniversityEgypt Department of Computer Science College of Computer and Information SciencesJouf UniversityKSA Department of Computer Science Faculty of Computers and InformationMenoua UniversityEgypt

This research presents,and claries the application of two permutation algorithms,based on chaotic map systems,and applied to a le of speech *** are the Arnold cat map-based permutation algorithm,and the Baker’s chaotic map-based permutation *** algorithms are implemented on the same speech signal ***,both the premier and the encrypted le histograms are documented and *** speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted ***,the original le is plotted against the encrypted le,using the spectrogram frequencies of speech signals with the signal *** permutation algorithms are used to shufe the positions of the speech les signals’values without any changes,to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption,e.g.,the time of both procedures,the encrypted audio signals histogram,the correlation coefcient between specimens in the premier and encrypted signals,a test of the Spectral Distortion(SD),and the Log-Likelihood Ratio(LLR)*** outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms(Baker and Arnold)are sufcient for providing an efcient and reliable voice signal encryption ***,the Arnold’s algorithm gives better results in most cases as compared to the results of Baker’s algorithm.

关键词： Arnold’s cat map chaotic maps permutation algorithms speech encryption Baker’s chaotic map

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A Comprehensive Approach for Early Detection of Leaf Infection 5th

A Comprehensive Approach for Early Detection of Leaf Infecti...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Kiruthika, A. Kumar, P. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

The agriculture industry is fundamental to the foundation of a country and is essential to the promotion of economic prosperity. It is crucial to ensure that Monitoring the health and detecting leaf infections in plants is crucial for maintaining sustainable agriculture. The actions taken to identify leaf infection represent a significant challenge in the agricultural industry. Manually recognizing plant diseases by analysing leaf images can be a time-consuming undertaking. In response to this challenge, continuous advancements in technology, particularly in image detection, have become instrumental. These advancements enable the accurate and prompt recognition of leaf infections and pests, facilitating the development of early treatment strategies. The paper objective is to precisely recognize diseases in leaf images. The essential steps in this procedure involve pre-processing, training, and recognition. Artificial Intelligence emerges as a ground breaking innovation, empowering the training of machines with image datasets to autonomously identify diseases in crops. Using this proposed methodology this system aims to improve the speed, accuracy, and overall effectiveness in identifying and managing crop infections across diverse agricultural settings through the utilization of machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agricultural economics

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Suguna, R. Deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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A novel AES–DES with improved Cheetah optimisation algorithm for secured medical data transmission in cloud environment

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Journal of Medical Engineering and technology 2024年第3期48卷 100-117页

作者： Joel, Gunde Manikanta Kasyap, Chaturvedula V. Narasimha Sidiq, Shaik Abubakar Riyaz, Jammimanu Santhi Sri, T. Sreedevi, Pogula Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram Guntur India Department of CSE Rajeev Gandhi Memorial College of Engineering and Technology Nandyal India

In recent years, transmitting medical data has been a regular process. Although strong, safe, and dependable encryption techniques are necessary for medical data, cryptography is largely a computational process. The research presents a selective encryption approach for the transfer of sensitive data. This study proposes a novel technique for selecting the optimal keys to offer more security to medical data. Initially, the medical data is encrypted using the hybrid AES–DES technique. To make an efficient encryption method, the most optimal keys are selected utilising an improved Cheetah optimisation algorithm (ICO). Finally, the keys are optimised, and the input medical data is safely kept in the cloud system according to the established model. As a result, the proposed approach utilises the Python tool to evaluate the results. The simulation results show that the proposed method outperforms others in terms of encryption time 96 s, decryption time 92 s, memory usage (16), and latency (0.006). © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Advance encryption standard (AES) data encryption standard (DES) improved Cheetah optimisation (ICO) hybrid AES–DES sub bytes transformation mix columns transformation

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CampusSight—college Management Automation Using Full Stack and RPA 12th

CampusSight—College Management Automation Using Full Stack ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, Machine Learning and Artificial Intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Towards Mental stress Detection in University Students Based on RBF and Extreme Learning Based Approach 1

Towards Mental stress Detection in University Students Based...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Anand, Taruna Alam, Mohammad Shabbir Akila, D. Yadav, Manjushri Janardan Mavliya, Anil Kumar Saran Kumar, A. Graphic Era Deemed to be University Department of Humanities and Social Sciences Dehradun India College of Computer Science and Information Technology Jazan university Department of Computer science Jizan Saudi Arabia Saveetha College of Liberal Arts and Sciences SIMATS Department of Computer Applications Tamil Nadu Chennai India International Institute of Business Studies Department of Management Bengluru India COER College of Nursing and Paramedical Sciences Uttarkhand Roorkee India Bannari Amman Institute of Technology Computer Science and Engineering Erode India

ISBN: (纸本)9798350371802

A lot of individuals associate stressful situations with bad experiences. It is concerning that mental health issues are so common, particularly among younger people. The once carefree generation is now under a great deal of stress. Modern society's elevated stress levels are associated with an array of serious health issues, such as an increased risk of suicide, heart disease, depression, and stroke. Within the scope of our research, we are attempting to quantify the degree to which students experience internet-related anxiety in the seven days leading up to an exam. Model training, feature selection, and preprocessing are all tremendously dependent on sequencing. Images with consistent size and intensity can be achieved by removing noise and blinks during this preprocessing step. Classifier performance improvement via feature selection aims to normalize and increase accuracy by utilizing favorable attributes. Feature selection is crucial during the training of a StressNet-C-BiLSTM model. When compared to two state-of-the-art algorithms, BiLSTM and CNN, the suggested technique outperforms them. The results demonstrated a significant improvement, with an accuracy rate of 97.58%. © 2024 IEEE.

关键词： Health risks

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Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

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computers, Materials & Continua 2022年第4期71卷 1459-1471页

作者： Anupam Garg Anshu Parashar Dipto Barman Sahil Jain Divya Singhal Mehedi Masud Mohamed Abouhawwash Computer Science and Engineering Department Thapar Institute of Engineering and TechnologyPatialaIndia School of Computer Science and Statistics Trinity College DublinDublinIreland University Institute of Biotechnology Chandigarh UniversityMohaliIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Autism Spectrum Disorder (ASD) is a developmental disorderwhose symptoms become noticeable in early years of the age though it canbe present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completelybut can be reduced if detected early. An early diagnosis is hampered by thevariation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergenceof deep learning approaches in various fields, medical experts can be assistedin early diagnosis of ASD. It is very difficult for a practitioner to identifyand concentrate on the major feature’s leading to the accurate prediction ofthe ASD and this arises the need for having an automated approach. Also,presence of different symptoms of ASD traits amongst toddlers directs tothe creation of a large feature dataset. In this study, we propose a hybridapproach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and preciseprediction of ASD. The proposed framework gives more accurate predictionalong with the recommendations of predicted results which will be a vital aidclinically for better and early prediction of ASD traits amongst toddlers.

关键词： Deep learning explainable artificial intelligence autism spectrum disorder machine learning

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Enhanced U-Net and PSO-Optimized ANFIS for Classifying Fish Diseases in Underwater Images

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International Journal of Fuzzy Systems 2024年第8期26卷 2518-2535页

作者： Khabusi, Simon Peter Huang, Yo-Ping Lee, Mong-Fong Tsai, Meng-Chun Department of Electrical Engineering National Taipei University of Technology Taipei10608 Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu88046 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung41349 Taiwan Department of Aquaculture National Penghu University of Science and Technology Penghu88046 Taiwan General Competency Center National Penghu University of Science and Technology Penghu88046 Taiwan

Fish diseases are among the major limiting factors to increase global aquaculture production. They lead to increased fish mortality, low breeding and growth rates, and low meat quality. The success of aquaculture is heavily dependent on the timely identification of disease. Therefore, we propose a fuzzy U-Net model to automatically identify fish disease from underwater images. U-Net is enhanced with multi-head channel and spatial attention and used to segment infected fish regions from fish disease images. Color pixel intensity features are then extracted from the localized regions, a form of guided feature extraction. Fuzzy C-means clustering is then used to find the cluster centroids and data distribution within the clusters, for the design of fuzzy membership functions. Moreover, the number of clusters are determined by silhouette score. Adaptive neuro-fuzzy inference system (ANFIS) is then trained, tested, and cross-validated for fish disease identification. The model parameters are optimized using particle swarm optimization (PSO) algorithm and compared with gradient-based methods. For image segmentation, the enhanced U-Net achieved a mean intersection over union (IoU) of 86.29%, mean pixel accuracy of 90.94%, mean precision of 93.58%, and mean recall value of 89.94% on 42 test images. Subsequently, ANFIS with PSO achieved overall superior performance on fish disease identification over gradient-based methods, with accuracy of 99.31%, precision of 99.00%, recall of 99.00%, and F1-score of 99.00%. The high-performance results of the optimized ANFIS confirm the robustness and efficacy of the proposed method to automatically identify fish diseases in aquaculture. © The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association 2024.

关键词： Fuzzy inference

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Blockchain Enabled Intelligent Surveillance System Model with AI and IoT 8

Blockchain Enabled Intelligent Surveillance System Model wit...

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8th IEEE International Conference on science, technology, Engineering and Mathematics, ICONSTEM 2023

作者： Sathyaseelan, K. Vyas, Tushar Madala, Ravikiran Chamundeeswari, Vijaya Rai Goyal, Himanshu Jayaraman, Raja Department of Information Technology Mallasamudram India Jecrc University Department of Computer Science and Engineering Rajasthan Jaipur India University of Cumberlands Ph.D Information Technology DurhamNC United States Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India St. Martin's Engineering College Department of Computer Science and Engineering Secunderabad India

ISBN: (纸本)9798350347791

In the New Year, Internet of Things (IoT) is industrializing in a few certifiable applications, for example, clever transportation, brilliant city to make human existence reliable. Extreme amounts of detecting information are being delivered from various sensors devices in the Modern IoT as a result of the growing industrialization of IoT. Understanding the constraints of flow innovations and the potential for future exploration techniques is necessary to the establishment of a secure and dependable information environment. A dishonest individual can connect with a trusted person through the blockchain, a decentralized digital ledger that tracks the conversation from start to finish. This report urges the medical care industry to deploy blockchain-coordinated digital protection in order to preserve clinically relevant things in light of the use of artificial intelligence in clinical settings. Applications based on blockchain may be able to precisely pinpoint the most serious, potentially dangerous mistakes in the medical sector. Blockchain-based decentralized information insurance safeguards patient health records from data theft. © 2023 IEEE.

关键词： Blockchain

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