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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sethuraman, Dhanush Ananya, Ela Joseph Jeffrey, J. Thangavel, Senthil Kumar Rangasami, P. Somasundaram, K. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita Vishwa Vidyapeetham Department of Social Work Coimbatore India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

This paper provides a framework for knowledge and action related to and about substance abuse, that integrates both capacity development and data-driven analysis for drug use analysis and modeling. In line with the National Action Plan for Drug Demand Reduction (NAPDDR) the framework focuses on a multi-pronged strategy. It then gives examples of so-called capacity-building exercises, such as those carried out by Amrita Vishwa Vidyapeetham in Tamil Nadu in 2020. These programs targeted key stakeholders, including healthcare professionals, police personnel, teachers, and social workers, focusing on preventive education, medical management, psychosocial issues, and rehabilitation strategies related to substance abuse. Pre- and post-training evaluations showed a strong increase in participants knowledge and treatment skills for substance use disorders. Second, the paper presents a data-driven, quantitative analysis of drug use patterns and related factors. Using a Support Vector Machine (SVM) algorithm, the study utilizes a dataset of drug consumption patterns to determine the factors which are most important for drug consumption. Importantly, the framework also include Explainable AI (XAI) methods to increase the interpretability and tracibility of the machine learning model. XAI approaches are used to interpret the model's decision process and to identify the most important factors in predicting the use of drugs. Incorporating insights from the capacity-building program evaluation and from the XAI-aided machine learning analysis, this framework provides an integrated end-to-end framework for drug user analysis and the generation of actionable recommendations for the design of targeted interventions and prevention strategies. © 2025 IEEE.

关键词： Support vector machines

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Multiple Linear Regression Algorithm-based Car Price Prediction 3

Multiple Linear Regression Algorithm-based Car Price Predict...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Swarnkar, Radhika Sawant, Rhea Harikrishnan, R. Srideviponmalar, P. Symbiosis International Deemed University Electronics and Telecommunication Engineering Symbiosis Institute of Technology Pune India Srm Institute of Science and Technology School of Computing Chennai India

ISBN: (纸本)9781665462167

Unlike "real estate", cars are depreciating assets, which means that their value over time and with use, decreases;because of this very reason, people find it very logical to purchase "pre-owned"cars instead of buying new ones at full price. After proper research, one may also find a vehicle priced at an amount that is less than what it is worth, making it a very good deal. This paper discusses different machine learning models for predicting the selling price of a used car which can be used by both sellers and buyers to know at what best price a vehicle should be sold, after taking into consideration parameters such as the kilometers driven, in which year the vehicle was purchased, no. of owners, fuel type, the present price of the car and so on. This model hence is a novel approach to tackle a specific, although widely seen problem, as it allows the people that are interested in buying a preowned car to get the best deal for it by making an informed decision. In this paper, different Machine learning algorithms such as random forest, decision tree, support vector machine, and multi-linear regression are used for this analysis. Multi-linear regression works best among all specified models owing to the fact that is the most accurate of all the models studied and has the highest accuracy percentage of 84.48%. In order to visualize the model performance with respect to the actual value, the graph is plotted. © 2023 IEEE.

关键词： Decision trees

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Virtual Assistant with Facial Expession Recognition

Virtual Assistant with Facial Expession Recognition

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Advances in computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Kanish Saxena S Janarthanan Himanshi Goyal School of Computing Science and Engineering Galgotias University India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

In Today’s world, Intelligent Virtual Assistants are used everywhere, and their base working principle is voice commands. And there are numerous technologies available for reading human facial expressions. So, in this paper, we will learn what happens if we combine both technologies. Does it better or create chaos? Artificial intelligence is gaining strength in the modern world. Every industry will soon use AI technology. AI has made great progress in recent years in identifying human emotions through voice, body language, facial expressions, etc. I’s significance stems from the fact that traditional human capabilities can now be studied inexpensively and at scale in the software. Every sector can use me to provide new possibilities and efficiencies. It is significant because it successfully overcomes a well-defined set of technological challenges. The technology enables us to complete competitive tasks with basic controls quickly. Every new voice-interactive technology we adopt deepens our reliance on artificial intelligence (AI) and machine learning from our smartphones, computers, televisions, and refrigerators. The connection between machines and humans that we had been seeing and dreaming about a few decades ago has been real thanks to voice assistants like Amazon Echo, Siri, Google Home, and Amazon Lexa. But today, the fact is undeniable and real. Voice search technology is the upcoming digital trend on the market; in certain cases, touch screens have already been replaced.

关键词： TV Military computing Prototypes Personal voice assistants Machine learning Touch sensitive screens Motion pictures

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Defending Authenticity: Real-Time Signature Verification with Deep Learning Models

Defending Authenticity: Real-Time Signature Verification wit...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Malireddy Charan Kumar Reddy Katragadda Megha Shyam Kandlapalli Aravind Sai Vishwas Hn Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Deep learning-based signature verification uses sophisticated neural network topologies to verify signatures. Deep learning models may be trained to discriminate between authentic and falsified signatures with a high degree of accuracy by utilizing methods such as vgg, xceptionmodels and Transfer Learning. These models are made to examine the complex characteristics and patterns found in signatures, which allows them to identify minute variations that could point to fraud. Deep learning methods provide a reliable and effective way to handle jobs involving the verification of signatures by utilizing large libraries and complex algorithms. The implementation of deep learning techniques for image processing and classification tasks, which can be adapted for signature verification applications. By preprocessing signature images, extracting relevant features, and training deep learning models on labeled data, the system can learn to differentiate between authentic and forged signatures effectively. Using deep learning involves the development and deployment of neural network models that can accurately authenticate signatures by analyzing their unique characteristics and patterns.

关键词： Deep learning Training Handwriting recognition Adaptation models Accuracy Neural networks Transfer learning Writing Feature extraction Data models

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Emergency Vehicle Navigation in Connected Autonomous Systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年第12期12卷 20670-20677页

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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A Bald Eagle Search Optimization Based Weighted Rank Aggregation Method for Microarray Data Classification 23

A Bald Eagle Search Optimization Based Weighted Rank Aggrega...

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15th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2023

作者： Li, Wei Shi, Tengfei Wang, Linjie Xie, Weidong Key Laboratory of Intelligent Computing in Medical Image MIIC Northeastern University Ministry of Education China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798400700385

The rapid development of microarray technology has generated a large amount of microarray data, and the classification of these data is meaningful for cancer diagnosis, treatment and prognosis. The classification of high-dimensional microarray data with small samples is a challenging problem, which usually requires feature selection methods to reduce the data dimensionality first. However, different feature selection methods usually generate different feature lists for the same data. Researchers need to choose among many feature selection methods, which reduces the research efficiency. Therefore, rank aggregation method is used to generate a optimal list by aggregating all ordered feature lists generated by different feature selection methods. It can combine the advantages of multiple feature selection methods and does not prefer a particular method, so it is more robust to outliers, noises and errors. In this paper, we propose a weighted rank aggregation method based on the Bald Eagle Search optimization. A positional weight is designed to emphasize the importance of the top features in the list, so that the distance between lists can be measured more accurately. In addition, we improve the Bald Eagle Search algorithm for optimizing rank aggregation method to obtain a optimal ordered list. The experimental results on six public microarray datasets indicate that the features selected by our method can significantly improve the classification performance. © 2023 ACM.

关键词： Feature Selection

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Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of Cortical Folding Patterns 22

Using Structural Similarity and Kolmogorov-Arnold Networks f...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Chen, Minheng Cao, Chao Chen, Tong Zhuang, Yan Zhang, Jing Lyu, Yanjun Yu, Xiaowei Zhang, Lu Liu, Tianming Zhu, Dajiang University of Texas at Arlington Department of Computer Science and Engineering United States Indiana University Indianapolis Department of Computer Science United States School of Computing University of Georgia United States

ISBN: (纸本)9798331520526

The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties need to be fully respected. To address this challenge, we propose a novel self-supervised framework for anatomical feature embedding of the 3HGs to build the correspondences among different brains. The core component of this framework is to construct a structural similarity-enhanced multi-hop feature encoding strategy based on the recently developed Kolmogorov-Arnold network (KAN) for anatomical feature embedding. Extensive experiments suggest that our approach can effectively establish robust cross-subject correspondences when no one-to-one mapping exists. The code is available at ***/m1nhengChen/SSE-CortexEmbed. © 2025 IEEE.

关键词： Mapping

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MultiJSQ:Direct joint segmentation and quantification of left ventricle with deep multitask-derived regression network

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 175-192页

作者： Xiuquan Du Zheng Pei Ying Liu Xinzhi Cao Lei Li Shuo Li Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui UniversityHefeiChina School of Computer Science and Technology Anhui UniversityHefeiChina Department of Neurology Shuyang Affiliated Hospital of Nanjing University of Traditional Chinese MedicineSuqianChina Department of Biomedical Engineering Case Western Reserve UniversityClevelandOhioUSA

Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular ***,the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the *** this study,the authors introduce a framework named MultiJSQ,comprising the feature presentation network(FRN)and the indicator prediction network(IEN),which is designed for simultaneous joint segmentation and *** FRN is tailored for representing global image features,facilitating the direct acquisition of left ventricle(LV)contour images through pixel ***,the IEN incorporates specifically designed modules to extract relevant clinical *** authors’method considers the interdependence of different tasks,demonstrating the validity of these relationships and yielding favourable *** extensive experiments on cardiac MR images from 145 patients,MultiJSQ achieves impressive outcomes,with low mean absolute errors of 124 mm^(2),1.72 mm,and 1.21 mm for areas,dimensions,and regional wall thicknesses,respectively,along with a Dice metric score of *** experimental findings underscore the excellent performance of our framework in LV segmentation and quantification,highlighting its promising clinical application prospects.

关键词： global image features joint segmentation and quantification left ventricle(LV) multitask-derived regression network

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Efficient Parameter Learning of Bayesian Network with Latent Variables from High-Dimensional Data 5

Efficient Parameter Learning of Bayesian Network with Latent...

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5th IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2023

作者： Wu, Xinran Chen, Xiang Yue, Kun Yunnan University School of Information Science and Engineering Kunming650500 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming650500 China

ISBN: (纸本)9798350332674

Bayesian network with latent variables (BNLV) plays an important role in the representation of dependence relations and inference of uncertain knowledge with unobserved variables. The variables with large cardinalities in high-dimensional data make it challenging to efficiently learn the large-scaled probability parameters as the conditional probability distributions (CPDs) of BNLV. In this paper, we first propose the multinomial parameter network to parameterize the CPDs w.r.t. latent variables. Then, we extend the M-step of the classic EM algorithm and give the efficient algorithm for parameter learning of BNLV. Experimental results show that our proposed method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Bayesian networks

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Securing IoT Devices and Data: An Analysis of Blockchain Technology and Mechanisms 15

Securing IoT Devices and Data: An Analysis of Blockchain Tec...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Thakur, Sujata Negi Sidana, Sagar Sinha, Anurag Akthar, Haniya Kumar, Biresh Kumar, Nitish Raj, Petheru Maheswaran, S. Al-Khayyat, Ahmed Graphic Era Hill University Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India School of Computing and information Science IGNOU New Delhi India Amity University Jharkhand Department of computer science and engineering Ranchi India Lovely Professional University Department of computer science and engineering Phagwara India Reliance Jio Platforms Ltd Bangalore India Kongu Engineering College Department of Electronics and Communication Engineering Tamilnadu 638060 India Islamic University Department of computer science and engineering Iraq

ISBN: (纸本)9798350370249

The Internet of Things, or IoT, has expanded substantially in recent years. Combining IoT and the Internet allows heterogeneous intelligent devices to process real-time data and conduct transactions. Thanks to the Internet of Things (IoT), almost everything and everything can be connected to the internet and covered. Smart housing, smart healthcare, smart irrigation, and many other uses for IoT are all common [2]. However, the IoT's dependability in terms of privacy and security presents significant development challenges. Consensus mechanisms, decentralization, smart contracts, and data encryption are all blockchain elements that are well suited for building IoT systems since they assist designers in preventing potential attacks and lowering transaction costs. Blockchain serves as a decentralized and open database platform, which can improve the performance of IoT security. We must consider the security challenges and concerns that come with blockchain technology in addition to its ability to provide us with more dependable and useful services. Blockchain technology creates a decentralised, tamper-proof ledger that can securely record and confirm transactions, offering a viable solution for improving the security of IoT devices. Using an explanation of the underlying blockchain technology, a discussion of the security issues that IoT devices must deal with, and a description of the possible advantages of using blockchain for IoT security, we investigate the utilization of blockchain innovation for safeguarding IoT gadgets in this paper. © 2024 IEEE.

关键词： Blockchain Consensus mechanism IoT

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