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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Anthonia Oluwatosin Adediran Samuel-Soma M. Ajibade Muhammed Basheer Jasser Faizah Mohammed Bashir Yakubu Aminu Dodo Yasni Nurul Huda Mohd Yassin Bayan Issa Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Darul Ehsan Malaysia Department of Decoration and Interior Design Engineering College of Engineering University of Hail Hail Kingdom of Saudi Arabia Architectural Engineering Department College of Engineering Najran University Najran Kingdom of Saudi Arabia Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

This study examines the academic field of artificial intelligence in Housing by examining the publications and indexed documents in Scopus and web of science (WOS) from 2000 to 2024. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI and Housing research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AI and Housing research. The Scopus and WOS search resulted in the discovery of 246 related publications on AI and Housing. With an increase from 1 publication to 68 publications within the observed years. Additionally, trends analysis indicated a significant increase of 5,016 in publications between 2000 and 2024. Articles are the most often utilized document type, including 172 of the overall amounts. The most prolific researchers in the field of AI and Housing are Ahmad, Chatterjee, Cho, Lee, Mehmood, Shaheen, Yang, and Zhao. All having published 3 documents each. Additionally, ten institutions have produced the highest number of published documents. The output of the affiliations active in the topic is primarily attributed to the funding, specifically the National science Foundation, which is the leading funding institution based in the United States and has contributed to 8 publications. An analysis of keyword co-occurrence revealed 4 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence and Housing. Future research in the field of artificial intelligence and Housing will focus on employing sophisticated machine learning, and neural network algorithms to precisely predict Housing transactions patterns while further enhancing housing market transactions across countries.

关键词： Machine learning algorithms databases Bibliometrics Neural networks Machine learning Market research Real estate industry Prediction algorithms Internet Socioeconomics

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Bridging the Gap with AI-Based Semantic Search Strategies

Bridging the Gap with AI-Based Semantic Search Strategies

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International Conference on Computing and Networking Technology (ICCNT)

作者： C. Vennila Prasad B. Chaudhari Ganesh Kumar Kantak Preeti Naval R Pramod Manish Nagpal Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Bridging the space with synthetic intelligence (AI)-based semantic seek strategies gives you practical techniques for navigating the ever-growing virtual facts space. With AI-pushed semantic search, users can query for valuable expertise in several databases, raising statistics discovery to a new degree of speed and efficiency. AI-pushed semantic search can identify valuable data in complicated record sets, bringing practical insights into sharper awareness while brushing off redundant content material from densely populated facts collections. With AI-pushed semantic seek, fact discovery is faster, more profound, informed, and more valuable. Lay users can enjoy the more advantageous manipulation of personal facts while expert workers receive the essential records to make more knowledgeable selections.

关键词： Costs Accuracy Semantic search Navigation databases Search engines Software artificial intelligence

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Equivariant diffusion for crystal structure prediction 24

Equivariant diffusion for crystal structure prediction

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Proceedings of the 41st International Conference on Machine Learning

作者： Peijia Lin Pin Chen Rui Jiao Qing Mo Jianhuan Cen Wenbing Huang Yang Liu Dan Huang Yutong Lu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and National Supercomputer Center in Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China and Institute for AIR Tsinghua University Beijing China National Supercomputer Center in Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP.

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Perceptive self-supervised learning network for noisy image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Li, Bo Zhang, Yanning Zhang, Shichao Zhang, David School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Xi’an710129 China Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To overcome these drawbacks, we propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) in this paper. PSLNet depends on a parallel network to remove noise and watermarks. The upper network uses task decomposition ideas to remove noise and watermarks in sequence. The lower network utilizes the degradation model idea to simultaneously remove noise and watermarks. Specifically, mentioned paired watermark images are obtained in a self-supervised way, and paired noisy images (i.e., noisy and reference images) are obtained in a supervised way. To enhance the clarity of obtained images, interacting two sub-networks and fusing obtained clean images are used to improve the effects of image watermark removal in terms of structural information and pixel enhancement. Taking into texture information account, a mixed loss uses obtained images and features to achieve a robust model of noisy image watermark removal. Comprehensive experiments show that our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal. Codes can be obtained at https://***/hellloxiaotian/PSLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. © 2023,

关键词： Electroencephalography

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Enhanced Water Security and Resilience in Drought-Prone Zones with IoT and SVM classifier for Early Warning Systems

Enhanced Water Security and Resilience in Drought-Prone Zone...

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

作者： P. Anandan V. Saillaja Rajesh Kambattan Kovarasan P. Padmaloshani Ishwarya M. V M. Rajmohan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Artificial intelligence and Data science Department Agni College of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The worldwide risk of droughts to water security and agricultural sustainability is growing. Using Internet of Things (IoT) technology and a Support Vector Machine (SVM) classifier to create drought-prone early warning systems is an innovative solution to these difficulties. Better water resource management and resilience in water-scarce locations are the study goals. The IoT infrastructure collects real-time data from sensors strategically positioned over the research area to monitor soil moisture, weather, and water levels. SVM classifiers, which are effective in classification, get this large dataset. The SVM model uses data to predict and classify drought events, alerting decision-makers and stakeholders. The system shows that this integrated strategy works. The IoT-SVM early warning system reliably predicts droughts and alerts authorities in advance, enabling proactive mitigation and intervention. The system's drought prediction accuracy helps drought-prone areas achieve water security and resilience by allocating resources, managing water sustainably, and reducing effects on agriculture and people. It uses IoT and machine learning to improve drought preparation and response, improving long-term water sustainability and resilience in water-scarce areas.

关键词： Support vector machines data integrity Droughts Alarm systems data models Sensors Internet of Things

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The Role of Information Technology in Enhancing Organizational Efficiency and Innovation

The Role of Information Technology in Enhancing Organization...

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International Conference on Information science and Communications Technologies (ICISCT)

作者： Ahmed Saleh Al-Samalek Hassan M. Al-Jawahry Raman Kumar Amit Verma Sumanpreet Kaur Nilesh Bhosle Department of Medical Laboratory Technics Al-Zahrawi University College Karbala Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Mechanical Engineering Rayat Bahra University Mohali India Journalism and Mass Communication Centre for Distance and Online Education (CDOE) Manipal University Jaipur Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India NIMS School of Computing Science and Artificial Intelligence NIMS University Jaipur Rajasthan India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

In this article potential of Information Technology (IT) in the improvement of organizational performance and innovation is discussed. It uses both quantitative questionnaires and qualitative case investigations to determine how different IT tools enhance operational and organisational innovation solutions. The research findings show that introduced IT solutions, including ERP systems, cloud technologies, and real-time analytics, increase efficiency by improving the effectiveness of processes; by decreasing expenses; and by increasing the speed of making decisions. Furthermore, lately, better technological support from IT technologies, including the artificial intelligence (AI) and the Machine Learning (ML), serves as enablers of innovation through new product development and into new markets. However, the study reveals the following various limitations: integration problems, organizational opposition, IT security threats, security threats of personal data, and technological life cycle. To overcome these challenges, change management, strong security solution, and regular IT assessment are the key factors to consider. It further underlines the significance of IT in organisational performance and offers guidelines for future studies to detect the sustainable impact of IT implementation and the contribution of new technologies in sustaining the competitive advantage in the future.

关键词： Technological innovation Cloud computing Information science Machine learning Real-time systems Product development Enterprise resource planning Security Information technology Guidelines

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Traceable and Authenticable Image Tagging for Fake News Detection

arXiv

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arXiv 2022年

作者： Meng, Ruohan Zhou, Zhili Cui, Qi Lam, Kwok-Yan Kot, Alex School of Computer and Software Nanjing University of Information Science and Technology China Institute of Artificial Intelligence and Blockchain Guangzhou University China Computer Science and Engineering Nanyang Technological University Singapore Lab Nanyang Technological University Singapore Centre for Computer Vision and Deep Learning University of Windsor Canada

To prevent fake news images from misleading the public, it is desirable not only to verify the authenticity of news images but also to trace the source of fake news, so as to provide a complete forensic chain for reliable fake news detection. To simultaneously achieve the goals of authenticity verification and source tracing, we propose a traceable and authenticable image tagging approach that is based on a design of Decoupled Invertible Neural Network (DINN). The designed DINN can simultaneously embed the dual-tags, i.e., authenticable tag and traceable tag, into each news image before publishing, and then separately extract them for authenticity verification and source tracing. Moreover, to improve the accuracy of dual-tags extraction, we design a parallel Feature Aware Projection Model (FAPM) to help the DINN preserve essential tag information. In addition, we define a Distance Metric-Guided Module (DMGM) that learns asymmetric one-class representations to enable the dual-tags to achieve different robustness performances under malicious manipulations. Extensive experiments, on diverse datasets and unseen manipulations, demonstrate that the proposed tagging approach achieves excellent performance in the aspects of both authenticity verification and source tracing for reliable fake news detection and outperforms the prior works. Copyright © 2022, The Authors. All rights reserved.

关键词： Image annotation

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Self-Replicating Imperative Node Evaluator for Improved Network Intrusion Detection 2

Self-Replicating Imperative Node Evaluator for Improved Netw...

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2nd IEEE International Conference on Innovations in High-Speed Communication and Signal Processing, IHCSP 2024

作者： Babitha, B.S. Banchhor, Chitrakant O. Bhuvaneshwari, K.S. Khurana, Saniya Senthilkumar, K.K. Marwaha, Shuthanshu School of Mangement - Ug Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641032 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Ece Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350368949

Cyber-attacks occur when someone attempts to gain unauthorized access to another person's or company's computer network. Network resources are considered compromised when several activities are made that potentially jeopardize their availability, anonymity, or reliability. Any time a system that detects intrusions notices questionable activity on an organization or system, for example, a potential breach of security policy, it notifies a management station. It is hard to efficiently identify threats in real-time due to the massive amount of data sent via cyber networks. The focus of this study is on developing an AI network for the purpose of detecting cyber threats. Checking user behavior for anomalies is an excellent way to spot fraud. When it comes to cyber defense, one of the biggest mysteries is how systems can detect previously unseen vulnerabilities and take action to mitigate or eradicate them. In order to stop cyber assaults, new cyber security measures are needed to identify malicious nodes before they interact. This study introduces an auto-triggering mode architecture and a replicating itself code for an aggressive node evaluator. As soon as it detects an intrusion, it may fix the network immediately, securing it and reducing the number of false alerts. The proposed model has a 98% success rate in both intruder identification and self-replication. Though progress has been made, there is still room for enhancement in reducing the number of false alarms. © 2024 IEEE.

关键词： Neural networks

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FPGA-Optimized Hardware Accelerator for Fast Fourier Transform and Singular Value Decomposition in AI

arXiv

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arXiv 2025年

作者： Ding, Hong Kang, Chia Chao Xi, SuYang Liu, Zehang Zhang, Xuan Ding, Yi School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Malaysia School of Computer and Data Science Xiamen University Malaysia Sepang Malaysia

This research introduces an FPGA-based hardware accelerator to optimize the Singular Value Decomposition (SVD) and Fast Fourier transform (FFT) operations in AI models. The proposed design aims to improve processing speed and reduce computational latency. Through experiments, we validate the performance benefits of the hardware accelerator and show how well it handles FFT and SVD operations. With its strong security and durability, the accelerator design achieves significant speedups over software implementations, thanks to its modules for data flow control, watermark embedding, FFT, and SVD. Copyright © 2025, The Authors. All rights reserved.

关键词： Intellectual property core

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