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5th International Conference on smart vehicular Technology, Transportation, Communication and Applications, vTCA 2022

作者： Fu, Zonglin Pan, Jeng-shyang Guo, Yundong snášel, václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava70032 Czech Republic

ISBN: (纸本)9789819908479

With the development of edge technology in the fields of transportation, wireless sensor networks, and the internet of things, more and more intelligent optimization algorithms are implemented in hardware structures. The Rafflesia optimization algorithm (ROA) is a novel intelligent optimization algorithm proposed recently. To observe its performance on hardware, this paper implements the ROA algorithm in a cooperative way of software and hardware, referred to as the FROA algorithm. To convenient testing and accelerated computing, the initialization module, fitness module, and update module of the FROA algorithm are deployed on the advanced RIsC machine (ARM) platform and the field programmable gate array (FPGA) platform, respectively. In the experimental part, we test the FROA algorithm on nine different benchmark functions. The results are compared with those implemented in software. The experimental results demonstrate the effectiveness and superiority of the FROA algorithm. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Wireless sensor networks

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big Data Analytics-based Diabetes Prediction Model for Identifying Internal Factors for Diabetes Mellitus 3

Big Data Analytics-Based Diabetes Prediction Model for Ident...

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3rd IEEE Delhi section Flagship Conference, DELCON 2024

作者： Mallika, C. vanitha, J. Kalaivani, K. selvamuthukumaran, s. E.G.S. Pillay Engineering College Department of Mca Tamilnadu Nagapattinam India School of Computer Science and Engineering Vit Vellore Vellore India A.V.C. College of Engineering Mannampandal Mayiladuthura India

ISBN: (纸本)9798331518592

Diabetes mellitus (DM) is a serious global health concern. According to the International Diabetes Foundation, 537 million individuals are living with DM worldwide, and this figure is expected to reach 643 million by 2030 and 783 million by 2045. Therefore, there is a pressing need for timely prognosis, identification, and management of DM and its impediments. This research proposes an efficient model to identify DM using an optimized support vector Machine (svM) algorithm. The proposed model implements the stochastic Gradient Descent (sGD) optimization technique to fine-tune the svM algorithm by updating hyperparameter. Thus, the cohesive sGD-svM approach is more appropriate for big databases related to conventional svM algorithms. The sGD-svM algorithm can apprise the system progressively as new data arrives, which is beneficial for constantly growing databases. The performance of our OsvM model is meticulously analyzed using the Pima Indian database. To assess the hybrid sGD-svM model, its classification enactment is compared with existing state-of-the-art machine learning-based diagnosis systems in terms of designated evaluation measures. The extensive results of the experimental analysis demonstrate that sGD-svM is a more competent model with 95.7% accuracy, 84.2% sensitivity, 86.9% specificity, and 91.7% precision. Further, it realizes a minimal false alarm rate (i.e., 2.0% false positive rate (FPR) and 1.4% false negative rate (FNR)). Therefore, this study can conclude that sGD-svM is the improved diabetes analytic tool as compared with other existing models found in the literature. © 2024 IEEE.

关键词： stochastic models

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Machine Learning Techniques For Twitter Data Using sentimental Analysis

Machine Learning Techniques For Twitter Data Using Sentiment...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOvA 2024

作者： Wangi, Kanchan Inamdar, Anupama Department of Computer Science and Engineering BLDEA's V.P. Dr. P.G. Halakatti College of Engineering and Technology Karnataka Vijayapura586103 India

ISBN: (纸本)9798331505134

In current era, the expressive growth of social media platforms like Twitter has made it an invaluable source for understanding public opinion and sentiment. This paper discovers various machine learning techniques applied to twitter data for sentiment analysis. The study also investigates into the preprocessing steps essential for Twitter data, such as tokenization, stop-word removal, handling emojis and dealing with slang. The results suggest that machine learning models, especially those utilizing word embeddings and attention mechanisms, outperform traditional models in capturing the nuanced sentiment expressed in tweets. Feature extraction techniques such as bag-of-Words (boW), TF-IDF are to represent the textual data in format suitable for machine learning algorithms. Investigated the effectiveness of different machine learning models including Logistic Regression, bernoulli Naive bayes, support vector Machine, K Nearest Neighbors, and Random Forest for sentiment classification. Evaluated using recall and F1 score, experimental results show efficacy of proposed model for twitter data. © 2024 IEEE.

关键词： Tweets

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Faster Region based Convolutional Neural Network for skin Lesion segmentation

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Intelligent Automation & soft Computing 2023年第5期36卷 2099-2109页

作者： G.Murugesan J.Jeyapriya M.Hemalatha s.Rajeshkannan Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennaiTamil NaduIndia Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and TechnologyChennaiTamil NaduIndia Department of Artificial Intelligence and Data Science Saveetha Engineering College(Autonomous)ChennaiTamil NaduIndia Department of Electronics and Communication Engineering St.Joseph’s College of EngineeringChennaiTamil NaduIndia

The diagnostic interpretation of dermoscopic images is a complex task as it is very difﬁcult to identify the skin lesions from the *** the accurate detection of potential abnormalities is required for patient monitoring and effec-tive *** this work,a Two-Tier segmentation(TTs)system is designed,which combines the unsupervised and supervised techniques for skin lesion *** comprises preprocessing by the medianﬁlter,TTs by Colour K-Means Clustering(CKMC)for initial segmentation and Faster Region based Con-volutional Neural Network(FR-CNN)for reﬁned *** CKMC approach is evaluated using the different number of clusters(k=3,5,7,and 9).An inception network with batch normalization is employed to segment mel-anoma regions *** loss functions such as Mean Absolute Error(MAE),Cross Entropy Loss(CEL),and Dice Loss(DL)are utilized for perfor-mance evaluation of the TTs *** anchor box technique is employed to detect the melanoma region *** TTs system is evaluated using 200 dermoscopic images from the PH2 *** segmentation accuracies are analyzed in terms of Pixel Accuracy(PA)and Jaccard Index(JI).Results show that the TTs system has 90.19%PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

关键词： skin cancer melanoma diagnosis clustering convolution neural network unsupervised segmentation deep learning

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Comprehensive Earth system Analysis for Advancing Community Resilience: Integrating Remote sensing with Hybrid GAN-RNN Techniques

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Remote sensing in Earth systems sciences 2024年第4期7卷 788-803页

作者： sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. by combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to springer Nature switzerland AG 2024.

关键词： AI-driven solutions Climate resilience Data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing synthetic data Temporal pattern recognition

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Analysis of brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective 1

Analysis of Brain Health Using Machine Learning and Artifici...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thirunagalingam, Arunkumar Usha, M.s. Geetha, s. Dhanamathi, A. Dhivya, s. Thendral, M.Tamil Business Intelligence and Reporting B. S. Abdur Rahman Crescent Institute of Science and Technology Department of CSE Chennai600048 India VS.B. ENGINEERING COLLEGE Department of Computer Science and Engineering Karur India Roever Engineering College Department of CSE Perambalur621220 India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India

ISBN: (纸本)9798331518578

The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all domains. In healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age;illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of 'brain-predicted age' as a biomarker of individual contrasts in the brain ageing process. brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings. © 2025 IEEE.

关键词： Electronic health record

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Experimental investigation of mechanical properties and multi-objective optimization of electronic, glass, and ceramic waste–mixed concrete

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Environmental science and Pollution Research 2024年第46期31卷 57158-57176页

作者： Mageswari, Deivasigamani Uma Kareemullah, Hakim Jithesh, Korothan boopathi, sampath Rachel, Paul Manoharan Premkumar Priya Ramkumar, Mathiyalagan siva Department of Science and Humanities RMK Engineering College Thiruvallur district Nadu Tamil601206 India Department of Electronics and Instrumentation Engineering B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Mahatma Gandhi College Kerala Iritty Kannur670703 India Department of Mechanical Engineering Muthayammal Engineering College Tamilnadu Namakkal637 408 India Department of Civil Engineering Saveetha School of Engineering Thandalam Tamil Nadu Chennai602107 India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore641021 India

The utilization of waste from various sources plays an important role in minimizing environmental pollution and civil construction costs. In this research, the mechanical properties of concrete were studied by mixing electronic waste (EW), glass powder (GW), and ceramic tile waste (CW). The effects of weight percentages of EW, GW, and CW are considered to investigate improvements in mechanical properties such as compressive strength (Cs), split tensile strength (sTs), and flexural strength (Fs) of concrete. Taguchi analysis has been applied to predict the optimum composition of waste mixing percentages. The Multi-Objective Optimization Ratio Analysis (MOORA) techniques are applied to estimate the optimum composition of mixing wastes for maximizing the Cs, sTs, and Fs of concrete. It was observed that 10 wt.% of EW, 15 wt.% of GW, and 30 wt.% of CW are predicted as the optimal mixing combinations to obtain a maximum compressive strength of 48.763 MPa, a split tensile strength of 4.178 MPa, and a flexural strength of 7.737 MPa, respectively. Finally, the predicted optimum waste-mixed weight percentages were used to examine the microstructure and various elements in the concrete using sEM and XRD analysis. When compared to conventional concrete, the optimum waste-mixed concrete has improved its compressive strength (38.453%), split tensile strength (41.149%), and flexural strength (36.215%). © The Author(s), under exclusive licence to springer-verlag GmbH Germany, part of springer Nature 2024.

关键词： Compressive strength

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Phasmatodea Population Evolution Algorithm based on spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-shyang Pan Mengfei Zhang shu-Chuan Chu Xingsi Xue václav snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(sPPE)to improve clustering *** sPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a variable Neighborhood search(vNs)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of sPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,sPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that sPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights sPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Research on the Application of Artificial Intelligence in Cybersecurity: Integrating Advanced Technologies to Improve Threat Detection and Response 5

Research on the Application of Artificial Intelligence in Cy...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： balaji, T.s. Nadanasabai, R. Ravikumar, Rajesh Jagadeesan sharanyaa, s. Meenal, R. Deepthi, Nandigam Poornima Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Vadapalani Campus Chennai India St.Josephs College of Engineering Department of Mba Chennai India Evernorth Health Services ChattanoogaTN37402 United States Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India V.S.B Engineering College Department of Electrical and Electronics Engineering Karur India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India

ISBN: (纸本)9798350376685

In the current age of digital change, the field of cybersecurity is faced with progressively advanced and enduring dangers. Conventional security measures are finding it difficult to keep up with the fast-changing nature of cyber threats. Artificial intelligence (AI) offers a significant chance to enhance threat detection and response systems by leveraging its sophisticated data processing and pattern recognition abilities. This study explores the incorporation of artificial intelligence (AI) technology in the field of cybersecurity, specifically aiming to improve the effectiveness of systems that identify and respond to threats. The paper commences by examining the present difficulties encountered by cybersecurity experts, which encompass the large number and diverse range of potential threat vectors, the covert and flexible nature of contemporary malware, and the limitations imposed by resource constraints on the efficacy of human-driven analysis. The text explores the current cutting-edge AI strategies used to tackle these difficulties, including anomaly identification through machine learning algorithms, predictive analysis using deep learning models, and threat intelligence collecting through natural language processing. The research also examines the distinct factors of AI in the cybersecurity field, such as the requirement for immediate decision-making, the significance of comprehending the model, and the possibility of hostile attacks on AI systems. This study seeks to analyse current literature and emerging trends to gain a deep understanding of how artificial intelligence (AI) can enhance cybersecurity. Additionally, it aims to identify the obstacles and deficiencies that must be overcome to effectively incorporate AI technologies into this crucial domain. To summarise, the research highlights the considerable capacity of AI to transform cybersecurity practices, providing a way to develop more robust and flexible defence methods against the numerous danger

关键词： Adversarial machine learning

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Landmark scene Classification Using vision Transformer 2

Landmark Scene Classification Using Vision Transformer

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2nd IEEE International Conference on Data science and Network security, ICDsNs 2024

作者： sangeetha, M. sunil, G. shnain, Ammar Hameed shruthi, b.s. Ahamed, A.U. shabeer V.S.B. Engineering College Department of Cse Karur India Sr University School of Computer Science & Artificial Intelligence Warangal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Jamal Mohamed College Affiliated to Bharathidasan University Department of Computer Science Tiruchirapalli India

ISBN: (纸本)9798350373110

In building the navigation software application, the Landmark plays an important factor in visual place recognition. There is major problem for performing the classification to visual recognition, due to change of environments like summer, winter, rainy season the place changes. To overcome the problem, the paper proposes a vision transformer as classification for place recognition. In vision transformer the first stage is considerations images are divided into various parts, then it converts into sequence embedding by moving it. To keep information at correct position, the embedding position is used in the parts and the sequence result is sent to various multihead attention layers for final result generation. For validating the proposal, we performed the vision transformer model on the two different datasets such as paris and oxford datasets. The result obtained by performing the vision transformer is demonstrated the other existing model in terms of accuracy in two datasets the given values are 90.63% and 87.30% respectively. © 2024 IEEE.

关键词： Application programs

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