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International Conference on Futuristic Technologies (INCOFT)

作者： Ravikumar V Ashwini R. Nawadkar Anupam Shukla Umesh. M. Daivagna Ramkumar Krishnamoorthy N. Thangarasu Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of uGDX ATLAS SkillTech University Mumbai Maharastra India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

Proposes a unique technique to enhance the accuracy of deep mastering fashions for time series predictions. By combining existing deep getting-to-know models into an ensemble, the authors could reduce the last prediction's mean absolute percent errors (MAPE) by using up to 16. The method assumes that distinct models can discover one-of-a-kind traits of a problem, and combining them into an ensemble can result in improved performance. The authors exemplified the use of the proposed technique with several one-of-a-kind real-world datasets. They showed that their method can outperform conventional time-collection forecasting fashions, which include ARIMA and LSTM, through a widespread margin. The paper offers valuable insights into the capacity of ensembles and is of interest to researchers and practitioners alike.

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Animal Behavior Monitoring Based on Sequence to Sequence Model

Animal Behavior Monitoring Based on Sequence to Sequence Mod...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Qing-Zhe Cui Xin Yuan Justine Partoon Alexandra L. Whittaker Li-Xiang Xu Lei Liu Peng Shi School of Computer Engineering and Science Shanghai University Shanghai China College of Artificial Intelligence and Big Data Hefei University Hefei China School of Electrical and Mechanical Engineering The University of Adelaide Adelaide Australia Zoos South Australia Adelaide Australia School of Animal and Veterinary Sciences The University of Adelaide Adelaide Australia Anhui Jiaoxin Technology Co. Ltd. Hefei China

ISBN: (数字)9798331528072

ISBN: (纸本)9798331528089

Object tracking is a core task in the field of computer vision, with wide-ranging applications in scenarios such as video surveillance and autonomous driving. This paper focuses on the application of the SeqTrack method, a sequence-to-sequence (Seq2Seq) model-based approach for object tracking, to animal behavior monitoring video datasets. The SeqTrack method leverages an encoder-decoder architecture to capture the temporal dependencies of objects, enabling accurate object localization and bounding box prediction. To validate the performance of SeqTrack in practical scenarios, we conducted comparative experiments on a self-made Adelaide Zoo dataset, comparing SeqTrack with three other mainstream object tracking algorithms: ToMP, OSTrack, and MixFormer. The experimental results show that SeqTrack outper-forms in terms of both precision and success rate, demonstrating its superior capability in handling challenges such as fast object movements, complex backgrounds, and partial occlusions. These findings indicate that SeqTrack is well-suited for high-precision tracking scenarios like animal behavior monitoring.

关键词： Location awareness Analytical models Accuracy Animal behavior Wavelet analysis Video surveillance Prediction algorithms Pattern recognition Object tracking Monitoring

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CovidXDetector: Deep learning based chest abnormality detection for covid radiography diagnosis

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AIP Conference Proceedings 2024年第1期2853卷

作者： Arun Pandian Rathinakumar N. Yuvaraj D. Premkumar Anjana George Joseph A. Feroz Khan K. R. Sri Preethaa Undergraduate Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

COVID-19 is a highly infectious disease caused by the coronavirus and has resulted in a global pandemic since 2019. The virus evolves, mutating into several very subtle variants to escape the conventional infection testing methods such as RT-PCR, leaving them obsolete against such new variants. Due to a shortage of resources and rapidly growing infection and mortality rate, a much more robust and more efficient way of testing and diagnosing COVID-19 was developed using deep learning algorithm-based solutions that analyze Chest Radiographs. Existing algorithms suffer from model bias and lack knowledge interpretability by classifying the given image into two classes: healthy or infected. CovidXDetector is a deep learning-based algorithm that detects and localizes Chest Abnormalities on Chest X-Ray Radiographs with critical feature annotations in the image that serves as clinical evidence to diagnose the diagnosis COVID-19. Based on Cascade R-CNN, it achieves an Average Precision score of 0.642 and outperforms YOLOv5 and SSD based detection algorithms by over 14% and 22%, respectively. This study affirms that our proposed model is a practical, easy-to-use and efficient solution for real-time diagnosis of COVID-19 using Chest Radiographs.

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Exploring the Impact of Random Distribution Choices on Particle Swarm Optimization: An Initial Analysis

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Procedia Computer science 2023年 225卷 4930-4942页

作者： Joanna Kołodziejczyk Yuliia Tarasenko Piotr Sulikowski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw 04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. żołnierska 49 71-210 Szczecin Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment.

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Alzheimer’s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach

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Computers, Materials & Continua 2021年第12期69卷 3531-3548页

作者： Nora Shoaip Amira Rezk Shaker EL-Sappagh Tamer Abuhmed Sherif Barakat Mohammed Elmogy Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Centro Singular de Investigaci’on en Tecnolox’ias Intelixentes(CiTIUS)Universidade de Santiago de Compostela Santiago de Compostela15782Spain Department of Information Systems Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Department of Computer Science and Engineering College of ComputingSungkyunkwan UniversityRepublic of Korea Department of Information Technology Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt

Alzheimer’s disease(AD)is a very complex disease that causes brain failure,then eventually,dementia *** is a global health problem.99%of clinical trials have failed to limit the progression of this *** risks and barriers to detecting AD are huge as pathological events begin decades before appearing clinical *** for AD are likely to be more helpful if the diagnosis is determined early before the final stage of neurological *** this regard,the need becomes more urgent for biomarker-based detection.A key issue in understanding AD is the need to solve complex and high-dimensional datasets and heterogeneous biomarkers,such as genetics,magnetic resonance imaging(MRI),cerebrospinal fluid(CSF),and cognitive *** an interpretable reasoning system and performing interoperability that achieves in terms of a semantic model is potentially very ***,our aim in this work is to propose an interpretable approach to detect AD based on Alzheimer’s disease diagnosis ontology(ADDO)and the expression of semantic web rule language(SWRL).This work implements an ontology-based application that exploits three different machine learning *** models are random forest(RF),JRip,and J48,which have been used along with the voting *** dataset was used for this *** proposed classifier’s result with the voting ensemble achieves a higher accuracy of 94.1%and precision of 94.3%.Our approach provides effective inference ***,it contributes to a real,accurate,and interpretable classifier model based on various AD biomarkers for inferring whether the subject is a normal cognitive(NC),significant memory concern(SMC),early mild cognitive impairment(EMCI),late mild cognitive impairment(LMCI),or AD.

关键词： Mild cognitive impairment Alzheimer’s disease knowledge based semantic web rule language reasoning system ADNI dataset machine learning techniques

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Effective Classification for Logs of Routers Based on MLP Neural Networks

Effective Classification for Logs of Routers Based on MLP Ne...

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International Conference on Knowledge and Systems engineering (KSE)

作者： Bao-Thien Nguyen-Tat Duy Khanh Nguyen Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam Laboratory for Computational Physics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Vietnam

This article discusses the classification of abnormal log messages from Juniper routers. Abnormal log messages reflect the unusual activities of these routers. Classifying abnormal log messages into different categories helps monitoring personnel quickly identify errors and take timely measures to protect the devices. We extract features from abnormal logs and use a Multilayer Perceptron (MLP) neural network model for classification. The MLP neural network model requires knowledge of the log data from Juniper routers so that the trained network can analyze and validate that knowledge. We collected log data from Juniper routers operating on SCTV's core network system, identified abnormal log messages, and classified them based on our experience and knowledge.

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Business Clouds Security: Crafting a Contemporary Adoption Framework

Business Clouds Security: Crafting a Contemporary Adoption F...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Abhishek Bhuva Dipen Bhuva A. Hema D. Anandhasilambarasan G. Gowri Mukesh Soni Department of Computer Science University of Massachusetts Boston Boston Massachusetts USA Department of EECS Cleveland State University Cleveland Ohio USA Department of MCA E.G.S Pillay Engineering College Nagaipattinam Department of Computer science and Engineering Karpagam Academy of higher education Coimbatore Department of Artificial Intelligence and Data science Karpagam Institute of Technology Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cloud computing is becoming more and more popular. Many businesses are making investments in this field, either for internal usage or to offer services to external customers. Ensuring a secure and reliable environmental solution over cloud space is crucial for protecting and safeguarding the transactions conducted by organisations.A cloud computing adoption framework (CCAF) security that works well with business clouds is presented in this *** management, encryption, and firewalling are the three key security approaches that form the foundation of CCAF multilayered security. It is built on improvements in business file sync and share systems. The purpose of the methodology was to illustrate the CCAF multilayered security’ s resilience. In 50 hours of nonstop assault, CCAF multilayered security managed to identify and stop 99.7% of trojans and viruses, and achieve ≥ 80% blocking. The results of the research have been confirmed, and CCAF multilayered security can increase cloud-based big data services’ velocity, amount, and dependability.

关键词： Cloud computing Computer viruses Software as a service Robustness Encryption Security Trojan horses

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Deep Learning Approaches for Solving Challenges in Computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： Computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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A data-Driven Near-Optimization Approach for Smart Parking Management Platforms 18

A Data-Driven Near-Optimization Approach for Smart Parking M...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Bai, Mingyan Zhong, Shenghua Yan, Pengyu Chen, Zhibin Zhang, Zhixian University of Electronic Science and Technology of China School of Management and Economics Chengdu China Chengdu Communication Investment Group Intelligent Parking Industrial Development Company Ltd Chengdu China NYU Shanghai Division of Engineering and Computer Science Center for Data Science and Artificial Intelligence Shanghai China China Telecom Research Institute Shanghai China

ISBN: (纸本)9781665440486

This paper addresses an allocation optimization problem of parking slots in a real-time parking reservation platform in which parking demands randomly show up. For each decision period of a finite horizon, the reservation platform allocates the available parking slots at hand for the random demands with different types specified by the parking duration to maximize the total revenue over the whole horizon. This paper presents a real-time reservation framework and formulates the problem as a stochastic programming model, considering the different type demands with unknown probability distributions. We propose a data-driven near-optimization approach entitled a two-sample average approximation (2-SAA) to determine the allocation scheme over the horizon. In the 2-SAA approach, the confidence interval of the total revenue is established by the out-of-sample resampling method. The results of the numerical experiment validate the performance of the proposed 2-SAA algorithm. © 2021 IEEE.

关键词： Stochastic systems

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Parkinson’s Disease Detection using Hand Drawn Spirals and Waves

Parkinson’s Disease Detection using Hand Drawn Spirals and ...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： Shashidhara H S Saish K Kalbag Tanya Mathias Srinivasa K G Niranjanamurthy M Department of Informaition Science and Engineering M S Ramaiah Institute of Techology and Engineering Affiliated to Visvesvaraya Technological University Belgaum Bangalore India Department of Data Science and Artififial Intelligece International Institute of Information Technology Naya Raipur Chhattisgarh India Department of Artificial Intelligence and Machine Learning B M S Institute of Technology and Management Affiliated to Visvesvaraya Technological University Belgaum Bangalore India

Parkinson’s disease, a neurodegenerative disorder, affects millions of people worldwide. Early detection is critical for effective treatment and management. Early detection of Parkinson’s disease is challenging due to the lack of accessible and non-invasive screening methods. Current diagnostic techniques often require specialized equipment and invasive procedures, limiting their feasibility for widespread screening. There is a need for cost-effective and accessible approaches that can accurately detect early stages of Parkinson’s disease. The model requires a high degree of precision in distinguishing between these patterns, which is a significant challenge to overcome. In this research work, we proposed a novel approach for detecting early stages of Parkinson’s disease by analyzing hand-drawn spirals and waves in photographs.

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