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2nd International Conference on Data science and information System, ICDSIS 2024

作者： Aluvala, Srinivas Kalshetty, Jagadevi N. Husain, Saif O. Kaur, Jagroop Thind, Harpreet Kaur Sr University School Of Computer Science And Engineering Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Punjabi University Department Of Computer Science And Engineering Patiala India Dayananda Sagar College Of Engineering Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350381665

The drug deals with every living organism on the earth, for classification of drugs based on their action is an important element in drug development. This research paper presents a machine learning model that predicts the action of a drug, utilizing a large drug network. The model integrates J-48 algorithm and random forest algorithm, and form the hybrid bagging technique with the neural network model demonstrating. The model uses the struck2vec data feature, which derived from the large network. Then the data features and dataset are trained on the hybrid bagging technique for drug classification according to the drug properties. The study's findings highlight the important of machine learning models in drug classification which offers a valuable tool for researchers and pharmaceutical companies in the drug development process. The output includes the top classes of drugs, the prediction of action and aiding in new drugs discovery with higher accuracy of 95.83% which is superior over other learning models. © 2024 IEEE.

关键词： Classification (of information)

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Artificial Intelligence and Machine Learning in Autism Detection: From Common to Rare Disorders 5th

Artificial Intelligence and Machine Learning in Autism Detec...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Gupta, Arshia Malhotra, Deepti Department of Computer Science and Information Technology Central University of Jammu J&K Samba181143 India

ISBN: (纸本)9789819725496

Autism is an attitude-driven issue that generally resulted in negative stigma toward anyone. Autism is related to a developmental disability that is caused because of differences in the brain. People with autism problem generally have social communication and interaction problems. It is not always that people having autism issue experience developmental disability;however, this issue can also originate from other diseases as well. Autism disorder is categorized into common and rare diseases. Under common autism disorder, autism spectrum and attention deficit hyperactivity disorder. Research has been conducted to detect and correct the issues associated with common autism disorders. AI and machine-learning-based mechanisms are available for the detection of common autism-related diseases. For rare, autism-related diseases, limited work has been done. For rare autism including Rett syndrome and childhood disintegrative disorder can be screened with tests. Rare AD screening tests however are time- and money-consuming. Techniques including machine learning are researched over with the help of which detection of ASD and ADHD and decide whether to get a clinical test for the same or not based on generated predictions. AD is not curable disease however technology-driven methodology can be used for early detection and avoiding additional harm to the body. In modern era, artificial intelligence and machine-learning-based mechanism have been evolved to complement the traditional clinical tests for the diagnosis of AD. The focus of this paper is to explore the AI and machine-learning-based mechanisms along with clinical tests that are used for detection of AD. Furthermore, this paper finds out the most significant mechanism used for the classification of AD-based disease. Rare disease however yet not explored with technology and hence open issues and challenges associated with rare diseases has been explored through this research. © The Author(s), under exclusive license to Sp

关键词： Diseases

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Pose invariant non-frontal 2D, 2.5D face detection and recognition technique

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International Journal of information technology (Singapore) 2023年第5期15卷 2603-2611页

作者： Thavani, Shiva Sharma, Sahil Kumar, Vijay Computer Science and Engineering Department Thapar Institute of Engineering and Technology Punjab Patiala India Computer Science and Engineering Department Punjab Engineering College Chandigarh India Information Technology Department Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

In this paper, the Pose Invariant Identity Recognition (PIIR) technique is proposed. It measures face resemblance using facial landmarks, which are further vigorous to pose disparity. The proposed technique utilizes the concepts of face frontalization and discriminative learning techniques. 3D Morphable Model is used in both techniques for pose invariant. The proposed technique is validated on CAS-PEAL face dataset. Experimental results signify that the proposed technique attains authentication accuracy of 90.24% on face datasets with 30° pan-angle. The proposed technique is further applied on 2.5D face dataset with variant facial positions and expressions. Results reveal that the proposed method can identify the face accurately. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： 2.5D depth-based face recognition Face reconstruction Feature extraction Non-frontal face recognition Pose-invariant identity recognition

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Medical Image Analysis Using Soft Computing Feature Selection and Classification of Skin Cancer 1

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International Conference on Modern Electronics Devices and Communication Systems, MEDCOM 2021

作者： Saraswat, Birendra Kumar Srivastava, Shipra Singh, Samender Takuli, Arun Kumar Saxena, Aditya Department of Computer Science and Engineering G.L.A. University Mathura India Department of Information Technology Greater Noida Institute of Technology Greater Noida India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Hi-Tech Institute of Engineering and Technology Ghaziabad India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

Adaptive resonance theory ARTI neural network (unsupervised learning) is being implemented for the classification of feature values with the objective of carrying out a comparative study with the performance of other technique including multi-layer feed forward network transform (supervised learning). A neural network is applied for the detection/identification of melanoma skin cancer. The images were collected from melanoma database is first processed and some distinguishable features were extracted by a MATLAB GUI. Two classes of the images were used: (a) benign, (b) malignant. A collection of interpreted binary input data, which is digitized from analog data after normalization is, then clustered adaptively with neural network ART-1. The goal of the project is to classify the feature dataset using supervised and unsupervised neural networks. To collect the feature set 1 implemented MATLAB image processing toolbox to create my own MATLAB functions. I created my own ARTI codes in MATLAB 701 (AKTI) to analysis which features are giving better efficiency using different vigilance parameter. Along with these supervised and unsupervised classification and pattern recognition, I am also trying to classify my own data-set of extracted features on Weka machine learning benchmarking software which is nothing but implements machine learning algorithms in JAVA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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End-to-end dynamic residual focal transformer network for multimodal medical image fusion

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Neural Computing and Applications 2024年第19期36卷 11579-11601页

作者： Zhang, Weihao Yu, Lei Wang, Huiqi Pedrycz, Witold College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Mathematics and Statistics Chongqing University Chongqing401331 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 2V4 Canada

Multimodal medical image fusion aims to improve the clinical practicability of medical images by integrating complementary information from multiple medical images. However, in traditional fusion methods, the fusion rules based on prior knowledge or logic usually cannot match the feature representation perfectly, which results in partial information loss. Furthermore, most deep learning-based fusion methods depend on convolutional operations, which only focus on local features and have limited retention of context information. To address the above issues, we propose an end-to-end dynamic residual focal transformer network for multimodal medical image fusion, termed DRFT. The DRFT framework is an end-to-end network with no need to manually design fusion rules. Firstly, the context-gated convolution is introduced to construct the context dynamic extraction module (CDEM) to extract the key semantic information more accurately from multimodal medical images. Then, a new residual transformer fusion module (RTFM) is designed by incorporating the focal transformer into the residual mechanism, which can not only extract the deep semantic features, but also adaptively learn the optimal fusion scheme. Finally, the nest architecture is employed to extract multiscale features. In addition, a new objective function consisting of global detail loss and fusion enhancement loss is designed to enrich the modal information in the fused image. Notably, the proposed network does not require the two-stage training strategy as opposed to the traditional encoder–decoder fusion structure. Extensive experimental results on mainstream datasets show that, compared with the state-of-the-art methods, the proposed DRFT delivers better performance in both qualitative and quantitative evaluation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework 31

When Computing Power Network Meets Distributed Machine Learn...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Yuan, Xinjing Pu, Lingjun Jiao, Lei Wang, Xiaofei Yang, Meijuan Xu, Jingdong Institute of Systems and Networks College of Computer Science Nankai University China University of Oregon Department of Computer Science United States School of Computer Science and Technology Tianjin University China

ISBN: (纸本)9798350399738

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings. © 2023 IEEE.

关键词： Integer programming

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Machine Learning Approach for Onion Leaf Disease Detection: A Case Study in Maharashtra, India 15

Machine Learning Approach for Onion Leaf Disease Detection: ...

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

作者： Amondkar, Madhavi Bhoite, Sachin Vidyalankar School of Information Technology Department of Information Technology & Data Science Mumbai India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science & Applications Pune India

ISBN: (纸本)9798331300579

Maharashtra is a remarkable state in India that is a major producer of onions, accounting for a sizeable portion of the country's total production. An ideal climate for onion growth is provided in areas like Nashik, Ahmednagar, and Pune Despite its importance, growing onions is filled with difficulties, the most significant of which being the frequency of illnesses that damage onion crops. Serious risks to onion production include diseases which are downy mildew, purple blotch, bacterial blight, white rot & basal rot which result in output losses and financial hardship for farmers. Machine learning models have become extremely effective tools for managing and forecasting agricultural crop diseases such as tomato, corn, grape etc. While nearly all crop leaf diseases may be predicted by these algorithms, very little or none has been done for onions in Maharashtra. There is almost no research done focusing on the onion disease found in different parts of Maharashtra. The purpose of the study is to assist farmers in Maharashtra in recognizing various diseases during the cultivation of onions by utilizing a machine learning model that combines CNN with image classification. Using a dataset of plant leaf diseases, this model ran with 95% accuracy. Additionally, given that the suggested model may be trained on any kind of leaf disease picture dataset and assist in disease type prediction, it can be suggested to work on an onion disease dataset as well. © Grenze Scientific Society, 2024.

关键词： basal rot CNN leaf disease machine learning Maharashtra onion disease purple blotch

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Incentive-driven and SAC-based Resource Allocation and Offloading Strategy in Vehicular Edge Computing Networks

Incentive-driven and SAC-based Resource Allocation and Offlo...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Guo, Jianmeng Zhou, Huan Zhao, Liang Chang, Wei Jiang, Tingyao College of Computer and Information Technology China Three Gorges University Yichang China Saint Joseph's University Computer Science Department United States

ISBN: (纸本)9781665494274

Vehicular edge computing (VEC) networks can provide low-latency services for vehicles. However, it is a great challenge for edge nodes to satisfy the computing tasks of all vehicles during vehicle peak hours. This paper studies the joint optimization problem of offloading strategy and resource allocation in a VEC network composed of road side units (RSUs) with computing resources, vehicle users and vehicle workers. In order to alleviate the computing pressure of the RSU, we use contract theory to encourage vehicle workers to execute computing tasks. At the same time, we propose a novel algorithm based on Soft Actor-Critic (SAC) to solve the system cost minimization problem considering vehicle users' satisfaction, RSUs' cost and vehicle workers' reward. Finally, we conduct extensive simulations in different scenarios, the simulation results show that our proposed algorithm has higher performance in reducing system cost compared with other benchmark methods. © 2023 IEEE.

关键词： Resource allocation

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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Application of Artificial Neural Networks inSatellite Imaging - A Systematic Review 4

Application of Artificial Neural Networks inSatellite Imagin...

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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Sharif, Hanan Rehman, Faisal Rida, Amina Nouman Ali, Chaudhry Zeeshan Zulfiqar, Rana Akram, Salman Kirn, Hina Hussain, Ayaz Iftikhar, Razia Information Technology Lahore Leads University Department of Computer Science Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Computer Science Khairpur Mir's Pakistan

ISBN: (纸本)9798350335316

Artificial Neural Networks (ANN) and deep learning have been instrumental in the advancement of technology around the world for about 50 years, but because of the high costs associated with developing an optimal training and testing dataset, researchers have had to deal with several issues such as segmentation of images with low spatial resolution, object recognition, classification, and their use in the processing of satellite images has yet to reach its full potential. This work includes a thorough assessment of a significant body of research literature as well as the most important publications released in the last decade. The IEEE digital library, science Direct, and SCOPUS system database indexing repository were the key sources for the review after applying various criteria, 386 publications pertaining to the case study were discovered. With 30 of them, grounds for exclusion and inclusion are discussed in further detail. Finding an upward trend in the level of research done in recent years indicates that this artificial neural network technology is getting more and more popular and curious. © 2023 IEEE.

关键词： Image segmentation

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