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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Hariharasudhan, S. Komarasamy, Dinesh Aravindh, S. Darshan, S. Saranya, S. Mohana Iyapparaja, M. Kongu Engineering College Department of Computer Science & Engineering Perundurai India VIT School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350349221

Heart illnesses are now an increasing occurrence, and regular human heart testing gets more and more significant. The Phonocardiogram (PCG), a useful diagnostic technique for examining heart sounds, offers insightful data regarding the clinical state of the heart. This research provides an algorithmic method employing PCG sound data to distinguish between abnormal and normal cardiac sounds. Both signal types undergo Cepstrum analysis, and the heart sounds' features are retrieved. Following that, a support vector machine classifier is trained and evaluated using these extracted features, with an accuracy of 95 percentage, generating a method for characterizing heart sound normal or abnormal for PCG signals. © 2024 IEEE.

关键词： Heart

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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A workflow based approach for task scheduling in cloud environment

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Materials Today: Proceedings 2023年 80卷 3305-3311页

作者： Patnaik, Harish Kumar Patra, Manas Ranjan Kumar, Rahul School of Computer Engineering KIIT University Bhubaneswar751024 India Department of Computer Science Berhampur University Berhampur760007 India

A Cloud computing environment provides a huge repository of computing resources which can be utilized for executing user tasks over the Internet. One of the challenges in such a computing paradigm is the optimal use of resources both from the cloud provider as well as consumer's point of view. Techniques have been developed to execute tasks with different level of complexity and size by assigning the tasks to the available resource pool which may span over a large geographical area. In this paper, we suggested a workflow approach to execute large number of interdependent tasks using a set of computing resources with heterogeneous capacity. The objective of this work is to allocate the tasks in such a way that the total time required to complete is minimized. The proposed technique is an extension of max–min algorithm applied on a Directed Acyclic Graph representing a set of interdependent tasks. The technique has been tested on standard scientific workflows and the results are found to be better compared to other similar works in terms of makespan. © 2021

关键词： Cloud computing

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Exploring the possibilities of integration of cyber-psychology for human behaviour in a smart city

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International Journal of Information and Communication Technology 2024年第2期25卷 150-167页

作者： Wen, Liping Ting, Zhou Zheng, Huang Alfred Daniel, J. Antonidoss, A. Department of Psychology University of Chinese Academy of Sciences Beijing100101 China Institute of Psychology Chinese Academy of Sciences Beijing100101 China Department of Medical Psychology School of Health Humanities Peking University Beijing100191 China Department of Computer Science & Engineering SNS College of Technology India Department of Computer Science and Engineering Hindustan Institute of Technology and Science India

The smart city idea differs between cities and nations. In all meanings and characteristics of a smart city, public involvement is the only thing that remains common. Therefore, it is a very significant field to study human behaviour and development in smart cities. This paper presents a framework for identifying qualities necessary for people to be classified as intelligent persons and to integrate these human behavioural characteristics in cyber technology. Human behaviour in a smart city has been analysed using the machine learning algorithm and big data analytics. The integrated machine learning and big data analytics framework (iML-BD) classifies the cyber behaviour of intelligent persons in a smart city by observing the cyber activities performed by the individuals. Furthermore, this paper handles the risk factors for cyber-acquired and cyber-dependant crime violence and abuse that vulnerable internet and public access devices using blockchain technology. Blockchain is a method of storing data that takes too long to alter, modify, or manipulate. A blockchain is an electronic accounting system that is reproduced and spread through the Bitcoin protocol’s entire communication network. The case study performed on iML-BD has resulted in the highest performance in terms of prediction accuracy of 94.98%. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Smart city

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Advanced Plant Species Classification with Interclass Similarity using a Fine-Grained computer Vision Approach

Advanced Plant Species Classification with Interclass Simila...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Suchithra Dhruthi, P.A. Somashekhar, B.M. Rani, N. Shobha Lin, Hong Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science Mysore India Gitam School Of Technology Gitam University Department Of Computer Science And Engineering Bengaluru India University Of Houston Department Of Computer Science And Engineering Technology Houston United States

ISBN: (纸本)9798350371567

Plant species classification holds immense importance across diverse sectors such as agriculture, environmental conservation, and botanical research. Accurate identification of plant species is fundamental for effective ecological management, agricultural practices, and biodiversity preservation. In this context, the research addresses the critical task of fine-grained plant classification, specifically emphasizing vegetable and fruit plants. Recognizing the significance of visual similarity analysis in plant sciences, a robust methodology leveraging Convolutional Neural Networks (CNN) is introduced. The hybrid CNN model combines the interpretability of VGG16 with the gradient-stabilizing benefits of ResNet50's residual connections, enabling nuanced classification of 23 plant classes. Achieving an outstanding 99% accuracy in plant class prediction, the model is not only highly accurate but also interpretable. Additionally, the incorporation of cosine similarity enhances the robustness of the approach, facilitating a leaf similarity search application. This innovative feature strengthens the identification of visually similar plant images based on extracted features, significantly contributing to the advancement of plant species classification methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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SHAPRFs: SHapley Additive eXplanation-Based Random Forests Algorithm for Classification Problems 4th

SHAPRFs: SHapley Additive eXplanation-Based Random Forests A...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Jain, Nishant Shukla, Shipra Department of Computer Science and Engineering School of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India

ISBN: (纸本)9789819726103

"Tree-based ensemble algorithms" (TEAs) are extensively employed for classification and regression problems. However, existing TEAs lag behind the trade-off between TEA interpretability and achieving cutting-edge accuracy when applied to real-time applications. This paper proposes a new TEA for bridging the gap between real-time applications and the trade-off between TEA interpretability and cutting-edge accuracy. The proposed algorithm is based on the SHapley Additive exPlanation (SHAP) value, which is one of the most advanced cooperative game solutions for quantifying each feature’s contribution to the model prediction. As a result, we call our proposed algorithm the SHapley additive explanation-based Random Forest algorithm (SHAPRFs). The proposed SHAPRFs compute the dominion of feature alliances using the SHAP value to determine the significance of each feature in the dataset. Following suit, the SHAPRFs divide the features into two subsets based on the importance of each feature. Then it employs the Roulette wheel selection algorithm to generate decision trees (DTs) in the forest. Five benchmark datasets are used to test the SHAPRFs algorithm. The experimental results show that the proposed SHAPRFs outperform three well-known and benchmark classifiers by a significant margin, namely, Breiman random forest (BRF), deep forest, and support vector machines (SVM). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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Multi-Scale HSV Color Feature Embedding for High-Fidelity NIR-to-RGB Spectrum Translation

Multi-Scale HSV Color Feature Embedding for High-Fidelity NI...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Huiyu Chen, Mo Yang, Xingxing Kang, Guosheng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9781665410205

This study investigates the actual impact of ChatGPT on human resource management (HRM) occupations and compares it with assessments from large language models (LLMs). Using data from the Occupational Information Network (O*NET), we developed a novel methodology combining real-world evidence from Google search results with evaluations from GPT-4, GPT-3.5, and Claude 3 Opus. Our findings reveal significant variations in the degree of exposure among HRM occupations. Training and Development Managers were the most exposed, with 86.52% of their tasks potentially automated by ChatGPT, while Labor Relations Specialists were the least exposed at 11.08%. Notably, LLM assessments consistently overestimated the exposure levels by an average of 30% compared to real-world evidence. This discrepancy highlights the importance of critically evaluating LLM capabilities in task automation. Our research contributes to understanding AI's impact on HRM practices and provides valuable insights for professionals adapting to the AI era. It also underscores the need for caution when relying solely on LLM assessments for workforce planning and development. © 2024 IEEE.

关键词： Human resource management

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Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis

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BMEMat(BioMedical engineering Materials) 2025年第1期3卷 158-181页

作者： Suparna Das Hirak Mazumdar Kamil Reza Khondakar Ajeet Kaushik Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for WomenHyderabadIndia School of Engineering and Technology Department of Computer Science and EngineeringAdamas UniversityKolkataIndia School of Technology Woxsen UniversityHyderabadIndia NanoBiotech Laboratory Department of Environmental EngineeringFlorida Polytechnic UniversityLakelandUSA

Machine learning(ML)and nanotechnology interfacing are exploring opportunities for cancer treatment *** improve cancer therapy,this article investigates the synergistic combination of Graphene Oxide(GO)‐based devices with ML *** production techniques and functionalization tactics used to modify the physicochemical characteristics of GO for specific drug delivery are explained at the outset of the *** is a great option for treating cancer because of its natural biocompatibility and capacity to absorb medicinal ***,complicated biological data are analyzed using ML algorithms,which make it possible to identify the best medicine formulations and individualized treatment plans depending on each patient's particular *** study also looks at optimizing and predicting the interactions between GO carriers and cancer cells using *** modeling helps ensure effective payload release and therapeutic efficacy in the design of customized drug delivery ***,tracking treatment outcomes in real time is made possible by ML algorithms,which permit adaptive modifications to therapy *** optimizing medication doses and delivery settings,the combination of ML and GO in cancer therapy not only decreases adverse effects but also enhances treatment accuracy.

关键词： artificial intelligence cancer diagnosis graphene oxide machine learning real‐time monitoring sensing

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of Electronics and Information Engineers.

关键词： Cavity resonators

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