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5th International Conference on Control, Communication and Computing, ICCC 2023

作者： Anjana Thampy, S. Jane Rubel Angelina, Jeyaraj Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Srivilliputtur India Lourdes Matha College of Science and Technology Department of Computer Science and Engineering Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Recently, sentiment analysis has attracted a good deal of interest from researchers. Each day, FB, Twitter, Weibo, and other social media, in addition to huge e-trade websites, generate big feedback, which can be used to investigate humans' critiques or emotions. Sentiment analysis will correctly analyze subjective records from this information. Deep learning becomes an effective machine learning approach which learns from a couple of layers of representations or features of the records and outputs a cutting-edge prediction effect. Deep learning overcomes all other machine learning techniques for sentiment analysis as well as offers different architectures for sentiment evaluation. Recently, RNN abbreviated as Recurrent Neural Networks as well as CNN abbreviated as Convolutional Neural Networks has been replaced with Transformer Language models in deep learning *** survey deals with contemporary architectures and challenges in that are in Sentiment Analysis as well as Natural Language Processing. © 2023 IEEE.

关键词： Sentiment analysis

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Detection and Classification of Blood Cancer using an Auto Encoder and a Regularised Extreme Learning Machine 14

Detection and Classification of Blood Cancer using an Auto E...

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

作者： Sharath, M.N. Rajesh, T.M. Surabhi, K.R. Sanjay, M. Nikshepa, T. Sowmya, S.R. Department of Computer Science and Engineering Rajeev Institute of Technology Hassan India Department of Computer Science and Engineering Dayananda Sagar University Bengaluru India Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bengaluru India

Leukemia is by far the most common type of blood cancer in humans. The length of time it takes to recover from an illness is determines by how quickly it is recognized and treated. Leukemia develops when the bone marrow generates an abnormally large amount of white blood cells. Because images of human blood samples are studied under the microscope, hematologists use techniques such as microscopic color imaging, image segmentation, grouping, and classification to diagnose patients as quickly and precisely as possible. Microscopy can identify blood cancer in visible and immature white blood cells in a variety of methods. Early and correct diagnosis of leukemia is critical for clinicians to properly treat patients. Following the submission of the input image, Image processing in image. © Grenze Scientific Society, 2023.

关键词： Image segmentation

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A Review on Binary Code Analysis Datasets 18th

A Review on Binary Code Analysis Datasets

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Huang, Zhijian Song, Shuguang Liu, Han Kuang, Hongyu Zhang, Jingjing Hu, Pengfei National Key Laboratory of Science and Technology on Information System Security Systems Engineering Institute Academy of Military Sciences Beijing China School of Computer Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031714696

Binary code analysis serves as the foundation for research in vulnerability discovery, software protection, and malicious code analysis. However, analyzing binary files is challenging due to the lack of high-level semantic information, leading to heavy dependence on analysts’ expertise and significantly impacting the efficiency of binary code analysis. Recent years has witnessed the blossom of machine learning models for binary analysis, but few researches address the problem of binary code datasets. In this paper, we review all the existing and available datasets, and make classification according to their application. We set up experiments to illustrate how dataset quality could affect the performance of machine learning models in binary function recognition. Based on the experimental evaluation, we present a discussion on the ground truth as well as quality evaluation problems for binary code datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Global video object segmentation with spatial constraint module

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Computational Visual Media 2023年第2期9卷 385-400页

作者： Yadang Chen Duolin Wang Zhiguo Chen Zhi-Xin Yang Enhua Wu Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of MacaoMacao 999078China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

We present a lightweight and efficient semisupervised video object segmentation network based on the space-time memory *** some extent,our method solves the two difficulties encountered in traditional video object segmentation:one is that the single frame calculation time is too long,and the other is that the current frame’s segmentation should use more information from past *** algorithm uses a global context(GC)module to achieve highperformance,real-time *** GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real ***,the prediction mask of the previous frame is helpful for the segmentation of the current frame,so we input it into a spatial constraint module(SCM),which constrains the areas of segments in the current *** SCM effectively alleviates mismatching of similar targets yet consumes few additional *** added a refinement module to the decoder to improve boundary *** model achieves state-of-the-art results on various datasets,scoring 80.1%on YouTube-VOS 2018 and a J&F score of 78.0%on DAVIS 2017,while taking 0.05 s per frame on the DAVIS 2016 validation dataset.

关键词： video object segmentation semantic segmentation global context(GC)module spatial constraint

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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Synergizing Coati Optimization and Hybrid Neural Architecture for Optimized Psychological Disease Prediction 2

Synergizing Coati Optimization and Hybrid Neural Architectur...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mekala, M. Reshma, N. Chinnaraj, Aparna Karthika, R. Sundareswari, K. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Rathinam Technical Campus Department of Information Technology Coimbatore India Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331534950

Predicting psychological diseases using machine learning has gained prominence in healthcare informatics. This research introduces an Optimized Enhanced Hybrid Approach (OEHA) by integrating the Coati Optimization Algorithm with the previously proposed Hybrid Hopfield Recurrent Neural Network (H2RN2) model. This synergy aims to optimize feature selection in psychological disease prediction, ultimately leading to improved accuracy and efficiency. This research also proposes an enhanced hybrid approach that combines the power of Convolutional Long Short-Term Memory (ConvLSTM) with hybrid hopfield attention layer to predict psychological diseases through analysis of clinical Electronic Health Records (EHRs). This approach can handle the multimodal data by using ConvLSTM while addressing the challenges LSTM alone might struggle to effectively capture spatial information in clinical data. By using attention layer with hopfield network, variable-length sequences are handled, parallel processing is achieved, vanishing gradient problem is vanished, leading to accurate prediction. © 2024 IEEE.

关键词： Electronic health record

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Vehicle Theft Detection Using Neural Pattern Recognition 3

Vehicle Theft Detection Using Neural Pattern Recognition

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jane Rubel Angelina, J. Subhashini, S.J. Kumar Rapuri, Anil Ravva, Vennela Rasala, Raghavendra Ray Adurgam, Sai Dcepak Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering TamilNadu Krishnankoil India SRM Madurai College for Engineering and Technology Dept of Computer Science and Engineering TamilNadu Sivagangai India

ISBN: (纸本)9798350322842

Finding stolen cars is becoming increasingly important in many urban regions. An automated system for scanning license plates can recognize vehicle numbers without the need for human interaction. This work proposes a vehicle theft detection system based on neural pattern recognition, gaussian filter, and equilibrium optimization. The proposed architecture has significant speedup and higher accuracy rates. The proposed number plate recognition method has a maximum accuracy rate of 94% and an average reduction in the processing time of 32%. Pattern recognition is the process of detecting regularities and patterns in data using machine learning. These analogies may now be uncovered via statistical analysis, historical data, or machine-generated knowledge. © 2023 IEEE.

关键词： Image segmentation

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Examining the Application of Machine Learning to Improve Organizational Performance in the Medical Setting 15

Examining the Application of Machine Learning to Improve Org...

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

作者： Shelke, Ganesh C. Dutta, Abhishek Katariya, Jitendra Kumar Sakhare, Nitin N. Anandhasilambarasan, D. Mahalakshmi, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350370249

This paper examines the capacity utility of machine gaining knowledge of within the clinical putting to enhance organizational overall performance and affected person effects. The paper begins by means of offering a top level view of ways gadget gaining knowledge of is being utilized in other industries and the way it is able to be applied inside the scientific placing. It then goes on to speak about the important thing challenges related to growing and deploying gadget learning inside the clinical putting which include statistics privateness and security, records availability, and they want for comprehensive expertise of clinical contexts. It examines the modern-day literature on machine studying inside the scientific placing to perceive satisfactory practices and proposes regions of destiny studies. In the end, the paper summarizes the implications from the studies, highlighting the capacity advantages and demanding situations that want to be addressed earlier than machine gaining knowledge of may be efficiently implemented in the medical field. Universal, this paper offers an informative evaluation of the capability programs of machine gaining knowledge of inside the clinical placing and emphasizes the need for greater studies in this place. © 2024 IEEE.

关键词： clinical gaining greater satisfactory situations

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient terahertz radiation generation from air plasma with mid- and near-infrared three-color femtosecond pulses

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Optics Express 2025年第11期33卷 22610-22631页

作者： Fu, Bintao Zhang, Zhiwen Liu, Zefu Wang, Kang Song, Liwei Li, Wenkai Kostin, Vasily A. Silaev, Alexander A. Zhuang, Longzhen Lu, Chenhui Liu, Yi School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Ultra-Intense Laser Science and Technology Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Gaponov-Grekhov Institute of Applied Physics Russian Academy of Sciences Nizhny Novgorod603950 Russia Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod603022 Russia School of Mechanical and Automotive Engineering Shanghai University of Engineering Science Shanghai201620 China

We report on the generation of the terahertz (THz) radiation from air plasma driven by a three-color femtosecond field with the fundamental pulse in the near- (1700 nm and 800 nm) and mid-infrared (3900 nm) ranges. With an inline optical setup for three-color pulse generation and relative phase control, we observed that the THz pulse energy can be enhanced up to 6 times compared with the conventional two-color excitation scheme, dependent on the incident fundamental pulse energy. The polarization of the enhanced THz pulse was found to be elliptical, with the second and third harmonics components of the three-color pump also being elliptically polarized. With the 3.9 µm driving pulses, the maximum THz pulse energy reaches 39.4 µJ with a conversion efficiency of about 1%. Based on the plasma current model, we analytically and numerically reproduced the THz energy enhancement, as well as its dependence on incident pulse energy. The THz enhancement can be attributed to the two additional ionization wave mixing channels introduced with the addition of the third harmonic. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Electromagnetic pulse

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