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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Kumari, J. Jesy Janet Thangam, S. Raja, A. Saleem Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering University of Technology and Applied Science-Shinas Shinas Oman

ISBN: (纸本)9783031648496

Around the world, vehicle accidents claim many priceless lives. To tackle this problem, the Vehicular ad hoc networks (VANETs) are the most effective solution. Effective communication between vehicular nodes in VANETs is extremely important because of the rapid mobility of vehicles and different network topology. Currently, VANET mostly serves the public security, stability, information for voyageurs, and traffic Management, control of congestion, guidance, etc. Chaos on roadways can occur at any moment, anyplace. It is a situation in which the flow of traffic regulations might cease to function. Collisions, maintaining roads, violation of legal rules, etc., so to evade the congestion in the surrounding region. VANETs represent a dynamic and challenging communication environment characterized by high mobility, intermittent connectivity, and rapid changes in network topology. Efficient and reliable routing is crucial for enabling diverse applications such as traffic management, safety communication, and infotainment services in VANETs. In this research, propose a multi-faceted approach to improve routing performance in VANETs. An adaptive routing algorithm that considers vehicle mobility patterns and dynamically adjusts communication routes based on the anticipated movement of neighboring vehicles such as Modified Ant Colony Optimization (MACO) technique. This adaptability enhances the robustness of the network against frequent topology changes and improves the overall packet delivery ratio, end-to-end delay, and network reliability compared to traditional routing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Vehicular ad hoc networks

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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

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Soft computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri Raman RajaLakshmi, N.R. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers create video reviews of automobiles on platforms for example YouTube. In realistic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content creators and the viewers as reflected in the comments section. The vulnerability to facing circumstances in real-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text information. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-CaR) dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sentiment analysis

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FARMER: Forecasting Agricultural Results with Machine Learning Evaluation and Regression 11

FARMER: Forecasting Agricultural Results with Machine Learni...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Chauhan, Naman Bharathi Mohan, G. Sulochana, V. Kumar, R. Prasanna Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Department of Computer Science and Engineering Chennai India Anna Administrative Staff College Dehradun India

ISBN: (纸本)9798350350357

Agriculture plays an important role, in Indias economy providing support to a portion of the population and contributing significantly to the GDP. It is crucial to predict crop yields in order to make decisions and promote sustainable agricultural practices. To achieve this a research study proposes an approach that utilizes machine learning techniques such as Decision Tree, Support Vector Machine (SVM) Random Forest, Multiple Linear Regression and Partial Least Squares Regression. The study carefully evaluates the performance of these models. Reveals some findings while comparing them to determine the best model. Multiple Linear Regression achieves the accuracy at 81.2% followed by Decision Tree at 83.5% SVM at 92.1% Random Forest at 91.5%. Partial Least Squares Regression at 85.3%. Notably SVM emerges as the performing model demonstrating its potential in improving precision, for predicting agricultural yields. Ultimately an Android application is developed using Kotlin and Android Studio for an user experience. © 2024 IEEE.

关键词： Multiple linear regression

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An Automated Identification Approach for Partial Discharge Detection Using Density-Based Clustering Without User Inputs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 310-320页

作者： Rahman, Md Anisur Ang, Li-Minn Seng, Kah Phooi Charles Sturt University School of Computing Mathematics & Engineering BathurstNSW2795 Australia University of the Sunshine Coast School of Science and Engineering Sippy DownsQLD4556 Australia Xi'an Jiaotong Liverpool University School of AI & Advanced Computing Suzhou215123 China Queensland University of Technology School of Computer Science BrisbaneQLD4000 Australia

A recently published state-of-the-art density-based clustering technique called ICFSFDP for partial discharge (PD) detection requires various sensitive user-defined input parameters. This article presents a parameter-free clustering technique called PDAutoClust for PD detection. The PDAutoClust algorithm can produce high-quality clusters for PD datasets without requiring user-defined input parameters. PDAutoClust produces high-quality clustering results by utilizing a vein-based density clustering approach. The vein of a cluster is produced by using multivariate kernel density estimation and a unique neighborhood set. We compared the performance of PDAutoClust against ICFSFDP and seven other state-of-the-art density-based and non-density-based clustering techniques by using four PD datasets in terms of adjusted rand index, normalized mutual information, F1-score, and purity. Another contribution of the article is a novel merging technique used with PDAutoClust to merge small non-viable clusters that a clustering technique may produce. PDAutoClust produces the final clusters for a dataset by merging the non-viable clusters that a clustering technique may produce. We also evaluate the performance of PDAutoClust with merging technique versus PDAutoClust without merging technique using four datasets. Simulation results for PDAutoClust with the merging technique show good performance compared to ICFSFDP and seven other state-of-the-art clustering techniques. We also performed an ablation study to demonstrate the importance of the steps involved in PDAutoClust. © 2020 IEEE.

关键词： Merging

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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Blockchain Based Verification of Educational and Professional Certificates 2

Blockchain Based Verification of Educational and Professiona...

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Singh, Anjali Chauhan, S.P.S. Goel, Amit Kumar Master of Technology in Computer Science and Engineering School of Computing Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665493932

Educational Documents and Certificates are a proof of professional achievements for anyone. Without these documents, it is not possible to start your career and hence play a very important role in everyone's professional life. Till date, Colleges, Universities and various educational institutes issue paper based degrees and certificates which are prone to get damaged or may be lost. The other drawback is that this process of issuing of degrees and manual process of verification of those documents by third parties is a time taking and cumbersome process. The employers take a lot of time for verifying these paper based degrees and certificates before they give the job offer. Forgery of paper based documents is also easier and has led to many educational scams. This paper aims to give the solution to the problems mentioned above through Blockchain Implementation. Block chain technology is not only limited to crypto currencies but is also of great applications in various fields like health, supply chain management, finance, etc. It has disrupted the traditional education system. This paper highlights as to how the educational certificates can be verified using the Ethereum platform and smart contracts. The traditional paper certificates would be converted to digital certificates on student's request, their hash value would be calculated using cryptographic hash functions and stored on Blockchain. A unique certificate ID and transaction hash value would be generated which would then be used to verify the certificates through a common platform. © 2023 IEEE.

关键词： Smart contract

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Application of Deep Learning in the Development of a Drug Discovery System 15th

Application of Deep Learning in the Development of a Drug Di...

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15th International Conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Praveen Kumar, B.V. Nageswara Rao, A. Tummala, Navya Deepthi Bikku, Thulasi Reddy Madhavi, K. Computer Science and Engineering Usha Rama College of Engineering and Technology Andhra Pradesh Telaprolu India Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India Computer Science and Engineering Vignan’s Nirula Institute of Technology and Science for Women Andhra Pradesh Guntur India Computer Science and Engineering Amrita School of Computing Amaravati Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9783031810824

Efficient drug discovery hinges on the accurate prediction of interactions between potential drugs and target proteins. Deep learning models have shown promise in this domain;however, their adoption can be challenging for individuals not well-versed in both computer science and cheminformatics. In response to this challenge, we present DeepPurpose, an extensive and user-friendly deep learning library tailored for drug-target interaction (DTI) prediction. DeepPurpose offers a range of features, including the ability to customize interaction types through chemical and protein encoders, as well as access to over 50 neural architectures. As the effectiveness of existing aseptic medications diminishes, the identification of viable DTIs becomes pivotal in the drug development process. DeepPurpose employs an encoder-decoder structure to model interactions, taking compound SMILES strings and pairs of protein amino acid sequences as inputs. The system generates a binding affinity score for the given compound-protein pair. To achieve this, DeepPurpose encodes input proteins and compounds using multiple deep learning encoders, combining their deep embeddings and passing them through a deep neural network decoder to determine binding propensity. DeepPurpose represents a valuable tool in the field of drug discovery, providing researchers with accessible and effective means to predict and explore potential drug-target interactions. Its user-friendly design and comprehensive functionality aim to bridge the gap between deep learning and DTI prediction for a wider audience of researchers and practitioners. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis

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International Journal of Information Technology (Singapore) 2024年 1-17页

作者： R, Annamalai Sindhu, Kolla Gnapika Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages ensemble techniques to enhance the precision and dependability of fetal plane classification. We address two pivotal classification tasks: first, the categorization of fetal planes into six distinct classes, encompassing critical regions such as the fetal abdomen, brain, femur, thorax, maternal cervix, and other areas. The second task focuses on the nuanced classification of fetal brain planes, further refined into trans-thalamic, trans-cerebellum, and trans-ventricular subtypes. To address these challenges, we harnessed the power of six distinct pre-trained models, rigorously training each for 50 epochs. Our results underscore the consistent superiority of InceptionResNetV2, DenseNet121, and Xception. Through a pioneering ensemble approach, we synergistically harnessed their capabilities, leading to enhanced classification performance. This research promises to augment the precision of maternal-fetal plane classification, with the potential to revolutionize clinical practice and healthcare research. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： DenseNet121 Ensemble learning Fetal planes Histogram equalization InseptionResNetV2 Median filter Ultrasonography

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Web Based Application to Recognize Diseases and Providing Consultant to Patients 3

Web Based Application to Recognize Diseases and Providing Co...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sathish Kumar, P.J. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Trichy India Sathyabama Institue of Science and Technology School of Computing Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitates knowledge from other fields, such as computer science and statistics, as this comprehension is frequently difficult. The need to pursue novel approaches places a heavier burden than normal on these fields. The variety of new strategies allows for a comprehensive investigation while avoiding specific elements. As a solution to this issue, a systematic evaluation of human illnesses with system mastering is proposed. This study centers on current techniques for system learning advancement applied to the prognosis of human diseases in the medical field in order to discover exciting trends, make light predictions, and assist in decision-making. This initiative includes a scientific chatbot that uses synthetic intelligence to answer patients' questions. This article explains a one-of-a-kind set of device learning algorithms that are used in healthcare programmes to provide excellent preferred choice assistance. This article is intended to cover a research gap in the development of practical choice aid structures for scientific applications. © 2024 IEEE.

关键词： Clinical research

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