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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Gesture-driven Innovation: Exploring the Intersection of Human-Computer Interaction and Virtual Fashion Try-On Systems 2

Gesture-driven Innovation: Exploring the Intersection of Hum...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Prakash, M. Arunkumar, Nithes Gokul, G. SRM Instititue of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9798350365269

Virtual Fashion Try-On systems revolutionize online fashion retail, offering immersive shopping experiences. This paper investigates integrating gesture-driven interaction into these systems to enhance engagement, realism, and usability. The paper highlights the importance of Virtual Fashion Try-On systems in addressing fit uncertainty and tactile interaction limitations in online shopping. It explores gesture-driven interaction to enrich virtual fashion experiences. The paper examines gesture recognition, covering hand gestures, body movements, and facial expressions. Various algorithms and techniques, including computer vision and machine learning, are discussed, addressing challenges like accuracy and real-time performance. Challenges and future directions in gesture-driven Virtual Fashion Try-On are discussed, covering technical hurdles, human-centered design, and ethical considerations. Recommendations for future research and development are outlined. © 2024 IEEE.

关键词： Virtual reality

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Automatic video colorization based on contrastive learning and optical flow

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Multimedia Tools and Applications 2024年第21期83卷 59985-60001页

作者： Xiao, Shuyu Wang, Yongfang Wang, Yihan Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China

Video colorization is the process of assigning reasonable color values to gray videos. Most of the existing video colorization methods use an image colorization algorithm directly regardless of correlation between frames in videos. Besides, these methods need reference, but it is difficult to obtain the guidance of users or reference images in practical applications. To overcome this limitation, we propose an automatic video colorization based on contrastive learning and optical flow. Contrastive learning optimizes the temporal correlation loss between the feature vectors of the gray video frame patches and the generated color video frame patches during training, which ensures the consistency of image content at the patch level. The optical flow module maintains the temporal consistency of colored videos by minimizing temporal correlation loss. In particular, the perceptual loss is introduced to improve the subjective colorization effect. Experiments show that the proposed algorithm outperforms the existing methods in terms of subjective quality. To reproduce the results, we release the source code at: https://***/orange-416/CLAC-GAN. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Optical flows

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Classification of Fetal Health on Cardiotocograph data using Machine Learning Techniques 15

Classification of Fetal Health on Cardiotocograph Data using...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Deepa, M.S. Sujithra, M. Department of Computing-Data Science Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798331300579

Pregnancy and fetus development is an incredibly intricate biological process that can go wrong when the health of fetus or the mother gets ***, India has the highest number of stillbirths, with an estimated 5,92,100 deaths per year, and a WHO estimated rate of 22 per 1000 total *** is one procedure used to check whether the fetus is growing as *** diagnostic procedure measures the mother's uterine contractions and the fetus' heartbeat, usually in the third trimester of pregnancy when the fetus heart is fully *** paper aims to classify the results into one of three states (physiological, suspicious or abnormal) based on cardiotocographic data using machine learning algorithms and compare the accuracies of different ML classification models such as logistic regression, k-nearest neighbor, decision tree, random forest classifier and support vector machine. © Grenze Scientific Society, 2024.

关键词： Support vector machines

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Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 273-275页

作者： Wenbo Li Baoling Ning the National Key Laboratory of Science and Technology on Space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China the School of Data Science and Technology Heilongjiang UniversityHarbin 150001China

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

关键词： fault introducing letter

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Creating a Blueprint for Success in First-Year computing

Creating a Blueprint for Success in First-Year Computing

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Kreimendahl, Frank Schuster, Micah Suresh-Menon, Durga School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

First-year computing is the most important pathway to undergraduate degrees and student success in computing, data science, engineering, and related fields. While there is an abundance of research in the area, we still lack retention of students in first-year computing. This is due to many factors including, but not limited to, 1) student math preparation and readiness from high school, 2) adjunct faculty teaching classes where no common coursework is employed, 3) faculty teaching the same courses in different ways, creating inconsistency in material covered, and 4) lack of tutoring for students who are struggling, behind, or do not understand the material. This paper presents task force results from a collaboration between faculty and academic support specialists. The paper focuses on two high-impact areas of improvement: standardizing curricula and building support scaffolding outside the classroom. The results, a comprehensive course blueprint, include core resources for a first-semester computing course and recommendations for identification and support of struggling students. © American Society for Engineering Education, 2023.

关键词： Students

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JobViz:Skill-driven visual exploration of job advertisements

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Visual Informatics 2024年第3期8卷 18-28页

作者： Ran Wang Qianhe Chen Yong Wang Lewei Xiong Boyang Shen School of Journalism and Information Communication Huazhong University of Science and TechnologyWuhanChina School of Computer Science and Technology Huazhong University of Science and TechnologyWuhanChina College of Computing and Data Science Nanyang Technological UniversitySingaporeSingapore Wuhan National Laboratory for Optoelectronics WuhanChina Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of EducationWuhanChina

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities ***,the majority of these job sites are limited to offering fundamental filters such as job titles,keywords,and compensation *** often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of ***,we propose well-coordinated visualizations to provide job seekers with three levels of details of job information:a skill-job overview visualizes skill sets,employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users’swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and *** using a real-world recruitment advertisement dataset collected from 51Job,one of the largest job websites in China,we conducted two case studies and user interviews to evaluate *** results demonstrated the usefulness and effectiveness of our approach.

关键词： Visual exploration Job advertisements Skill-driven

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A Comprehensive Study on Current Trends in Unsupervised Machine learning Algorithms and Challenges in Real World Applications 14

A Comprehensive Study on Current Trends in Unsupervised Mach...

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14th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Velvadivu, P. Sujithra, M. Priyadharshini, R. Department of Computing - Data Science Coimbatore Institute of Technology India

In this technology fueled world, everyone is using smart devices, electronic gadgets, wireless products etc. and huge amounts of data are being generated, collected, and stored in the databases. To efficiently process and intelligently analyze the huge amount of data, the knowledge about subfield of Artificial Intelligence that is, Particularly Machine learning (ML) is required. There are various types of machine learning and its algorithms have been introduced to handle real world scenarios. This paper discusses the Comprehensive survey based on methodologies, techniques, algorithms, applications, and challenges faced by unsupervised machine learning and how unsupervised learning techniques can be helpful in real world business and environment. Thus, this study’s key contribution is explaining the principles of different unsupervised machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. © Grenze Scientific Society, 2023.

关键词： Unsupervised learning

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Machine learning-based gene expression biomarkers to distinguish Zika and Dengue virus infections: implications for diagnosis

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VirusDisease 2024年第3期35卷 446-461页

作者： Zeba, Ayesha Rajalingam, Aruna Sekar, Kanagaraj Ganjiwale, Anjali Department of Life Science Bangalore University Laboratory for Structural Biology and Bio-Computing Department of Computational and Data Sciences Indian Institute of Science

Zika virus (ZIKV) and Dengue virus (DENV) infections cause severe disease in humans and are significant socio-economic burden worldwide. These flavivirus infections are difficult to diagnose serologically due to antigenic overlap. The phylogenetic analysis shows that ZIKV clusters with DENVs at a higher node of the phylogenetic tree with significant genomic and structural similarity. Our study aims to identify gene biomarkers for the classification of Dengue and Zika viral infections using machine learning algorithms and bioinformatics analysis. The gene expression count matrix for single-cell RNA sequencing dataset GSE110496 was analyzed using binary classifiers, namely Logistic regression, Support Vector Machines, Random Forest, and Decision trees. The GSE110496 dataset represents a unique study of the transcriptional and translational dynamics of DENV and ZIKV infections at 4-, 12-, 24-, and 48-h time points for human hepatoma (Huh7) cells. Out of which 24-h time point has been analyzed in this study, at the optimal threshold of viral molecules. Feature selection was performed using two different approaches Random Forest Classifier (RFC) for gene ranking and Recursive Feature Elimination (RFE). Out of which RFE, showed more accuracy and precision. The classification accuracy of 89.4% and the precision of 90% were obtained using selected 10 gene features. SCY1 Like Pseudokinase 3 (SCYL3), Chromosome 1 Open Reading Frame 112 (C1orf112), Complement factor H (CFH), Heme-binding protein 1 (HEBP1), Cadherin 1 (CDH1), Nibrin (NBN), Histone deacetylase 5 (HDAC5), nuclear receptor subfamily 0, group B, member 2 (NR0B2), Annexin A9 (ANXA9) and Alcohol dehydrogenase 6 (ADH6) are the proposed gene biomarkers in this study. The functional analysis of the reported biomarkers was performed using KEGG and GO with the WEB-based Gene SeT AnaLysis Toolkit (WebGestalt). The relationship of the selected biomarkers with DENV and ZIKV infections analyzed using a gene–gene interaction n

关键词： Biomarkers Dengue Machine learning Single cell RNA sequencing Zika

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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