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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU

arXiv

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arXiv 2023年

作者： Liu, Haoran Li, Peng Liu, Mingzhe Wang, Kaimin Zuo, Zhuo Liu, Bingqi The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China The School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China The Engineering & Technical College of Chengdu University of Technology Leshan614000 China The Southwestern Institute of Physics Chengdu610225 China The School of Mechanical Engineering Chengdu University Chengdu610106 China

This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron performance. Experimental results substantiated that Tempotron serves as a formidable classifier, adept at accomplishing high discrimination accuracy on both AmBe and time-of-flight PuBe datasets. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting its hyperparameters. Moreover, the study addressed the constraints and potential avenues for future development in utilizing the Tempotron for pulse shape discrimination. The dataset used in this study and the GPU-based Tempotron are publicly available on GitHub at https://***/HaoranLiu507/TempotronGPU. © 2023, CC BY-NC-ND.

关键词： Gamma rays

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Fuzzy Regular Least Squares Twin Support Vector Machine and its Application in Fault Diagnosis

SSRN

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SSRN 2022年

作者： Zhou, Chengjiang Li, Hao Yang, Jintao Yang, Qihua Yang, Limiao He, Shanyou Yuan, Xuyi School of Information Science and Technology Yunnan Normal University Kunming650500 China The Laboratory of Pattern Recognition and Artificial Intelligence Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China

Objective function ignores the generalization error in LSTSVM, and training overfitting results in poor generalization performance. All sample labels are considered deterministic, however, some samples contain outliers affected by noises, which leads to low reliability. A recognition method based on fuzzy regular LSTSVM (FRLSTSVM) is proposed. Firstly, L2 norm regular term is introduced into objective function to improve the generalization performance. Secondly, outliers of samples are detected through support vector domain description (SVDD), which improves outlier detection accuracy. Then, a membership degree S3 is constructed to give the outliers a suitable membership degree, reducing the impact of outliers on results. Finally, FRLSTSVM is extended to a multi-classification model by one versus one (OVO) and binary tree (BT) strategies, and it is combined with an improved multiscale fluctuating Rényi dispersion entropy (IMFRDE) for fault diagnosis. The results show that the method has stronger generalization, lower sensitivity to parameters and higher reliability. © 2022, The Authors. All rights reserved.

关键词： Failure analysis

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Advancement of artificial Intelligent and Its Impact on Medicinal Treatment and Pharmaceutical Industries

Advancement of Artificial Intelligent and Its Impact on Medi...

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International Conference on science technology engineering and Management (ICONSTEM)

作者： Jenifer. A Syed Imran Ahmed Sangeetha G A. Prabhu Chakkaravarthy Ashok Kumar Ruhi Bakhare Department of Electrical and Electronics Engineering RMK Engineerinh College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering MJ college Hyderabad Telangana India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Thiruvallur Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of CSE Dr. Ambedkar Institute of management studies and research Deekshbhoomi Maharashtra India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In an unanticipatedly short period, a patient may seek advice from a computer prior to seeing a doctor, thus establishing the” standard” medical procedure of the future. Thanks to developments in artificial intelligence (AI), the days of misdiagnosing illnesses and treating their symptoms instead of their underlying causes may soon be over. It's possible that something like this will occur soon. To accommodate a complete 3D representation of an organ, imagine how much space would need to be cleared from your laptop or how many years' worth of blood pressure records would need to be eliminated. The image would require both of these things to be made possible. More uses of artificial intelligence and high performance data-driven medicine are made possible by the growing body of data generated in clinics and preserved in electronic medical records as a result of routine testing and medical imaging. These applications have led to the development of methods used by researchers and medical practitioners to address clinical concerns throughout time, and this trend is anticipated to continue.

关键词： Three-dimensional displays Portable computers Medical services artificial intelligence Standards Optimization Biomedical imaging

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Development of Recommender Systems for Better Services and Products using data science

Development of Recommender Systems for Better Services and P...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Padmapriya Thamizhamuthu R S. Jagadeesh D. M. Kalai Selvi M. Ahsan Shariff Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sridevi Women’s Engineering College Hyderabad Telangana India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India M. Tech - Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Online social networking and e-commerce are becoming increasingly popular. Recommender Systems (RS) let users find relevant information from several possibilities. Internet applications currently need RS. This technology uses huge data to provide customized suggestions to improve customer happiness. Concerns and ideas help customers choose items. Sentiment Analysis (SA) may increase RS recommendation accuracy by improving user behaviour, views, and responses. RS solves information overload in information retrieval, but data sparsity remains a big problem. SA is notable for reading text and expressing user preferences. It helps E-Commerce to monitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recommendation approach to increase RS accuracy and correctness. The hybrid approach beats standard models in several assessment criteria. Modern retailing businesses’ e-commerce operations are impossible without RSs. The content-based and context-aware techniques are hybridized for providing promising results. Content-based approaches connect consumers to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithms propose customized items with minimal use history. These systems use case-based recommendations or limitations to make recommendations. Finally, ensemble recommender systems combine data source prediction power.

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A Comprehensive Approach to Predict the Autism Spectrum Disorders using Deep Learning Mechanism

A Comprehensive Approach to Predict the Autism Spectrum Diso...

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International Conference on Smart Structures and Systems (ICSSS)

作者： Gayathri S Deepa A K.B. Aruna P. Vijayaraghavan V. Samuthira Pandi K. Pavithra Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India

Children often suffer from Autism Spectrum Disorder (ASD), a neurological disease. With stereotypical behavior and difficulties in social communication, ASD cannot be defined by chronological age alone. It is speculated that this condition results from malfunction in the brain caused by the interplay of several ecological, inherited and regulatory variables. More than one percent of the world's population has ASD, prompting policymakers, educators, and health care professionals to develop plans to combat the condition. Maximizing the potential of patients with autism demands a multidisciplinary approach. Therefore, it is crucial that various care models complement one another and do not cause the patient any unnecessary anxiety. Natural therapies have the potential to significantly enhance the long-term success of conventional treatments including psychotherapy, Occupational-Therapy (OT) and PT. According to the progression of symptoms, ASD can take on a number of different forms. However, there is still no agreed-upon gold standard therapy for ASD. If a systematic method is adopted, assessing a kid with suspected ASD is straightforward. There are substantial gains in interpersonal interaction, replication, as well as intellectual and adaptive performance following initial treatment. In this paper, a novel deep learning based prediction approach is designed called as Intelligent Learning Scheme to Predict Autism (ILSPA). This algorithm is cross-validated with the conventional learning approach called Deep Neural Network (DNN) to evaluate the performance and efficiency of the proposed scheme. The resulting scenario provides the proper proof to the efficiency of the proposed algorithm called ILSPA.

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Structure-Aware DropEdge Towards Deep Graph Convolutional Networks

arXiv

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arXiv 2023年

作者： Han, Jiaqi Huang, Wenbing Rong, Yu Xu, Tingyang Sun, Fuchun Huang, Junzhou Department of Computer Science and Technology Tsinghua University Beijing China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Tencent AI Lab Shenzhen China Department of Computer Science and Engineering The University of Texas Arlington United States

It has been discovered that Graph Convolutional Networks (GCNs) encounter a remarkable drop in performance when multiple layers are piled up. The main factor that accounts for why deep GCNs fail lies in over-smoothing, which isolates the network output from the input with the increase of network depth, weakening expressivity and trainability. In this paper, we start by investigating refined measures upon DropEdge—an existing simple yet effective technique to relieve over-smoothing. We term our method as DropEdge++ for its two structure-aware samplers in contrast to DropEdge: layer-dependent sampler and feature-dependent sampler. Regarding the layer-dependent sampler, we interestingly find that increasingly sampling edges from the bottom layer yields superior performance than the decreasing counterpart as well as DropEdge. We theoretically reveal this phenomenon with Mean-Edge-Number (MEN), a metric closely related to over-smoothing. For the feature-dependent sampler, we associate the edge sampling probability with the feature similarity of node pairs, and prove that it further correlates the convergence subspace of the output layer with the input features. Extensive experiments on several node classification benchmarks, including both full- and semi- supervised tasks, illustrate the efficacy of DropEdge++ and its compatibility with a variety of backbones by achieving generally better performance over DropEdge and the no-drop version. Copyright © 2023, The Authors. All rights reserved.

关键词： Drops

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Energy Efficient Clustering for Equating the Load in Wireless Sensor Network

Energy Efficient Clustering for Equating the Load in Wireles...

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Augmented intelligence and Sustainable Systems (ICAISS), International Conference on

作者： D. K. Karthika P. D. Selvam R. Hemalatha A. Sathiya Department of Information Technology Sri Sai Ranganathan Engineering College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Shree Sathyam College of Engineering and Technology Salem Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India

Wireless Sensor Network (WSN) contains numerous sensor nodes and energy management is a major significant problem in WSNs. To solve this issue, Energy Efficient Clustering for equating the Load (EECL) is proposed. The objective of the EECL strategy is to improve the Quality of Service and enhance energy efficiency. The sleep and awake scheduling is employed to minimize the unwanted sensing the energy utilization. The multi-level clustering strategy is discussed in this work provides single-hop communication inside the cluster utilizing link correlation and a Time Division Multiple Access (TDMA) time slot. By using network coding, Cluster Head (CH) multi-hop communication is effectively managed. Furthermore, Queue Priority Indicator (QPI) as well as Geographic Priority Indicator (GPI) are also utilized to choose the optimal forwarder node as well as discover the competent route. The QPI is applied for equating the load as well as energy utilization. The EECL approach has been simulated and examined by applying the network simulator-3, and the outcomes have improved the effectiveness of the CH round and node lifetime.

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

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science, technology, engineering and Management (ICSTEM), International Conference on

作者： Gautam Solaimalai J. Maria Shanthi Sampath Kumar S Priyanka Khabiya K. Geetha Gokul Talele US. Bank Software Engineering Manager Georgia United States of America Department of Artificial Intelligence and Machine Learning J B. Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Engineering Mandsaur University Mandsaur Madhya Pradesh India Department of Computer Science G. T.N Arts College Dindigul Tamil Nadu India Department of Data Science Organization name Accenture Pune Maharashtra India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks.

关键词： Reviews Virtual assistants Propulsion Natural language processing Distance measurement

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