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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Kainat Khan Rahul Katarya Department of Computer Science & Engineering Big Data Analytics and Web Intelligence Laboratory Delhi Technological University New Delhi India

ASD or autism spectrum disorder is a critical neuro-developmental disorder that hinders an individual's capability of social communication and interaction. This disorder has acquired considerable attention and importance due to its ubiquity among individuals covering all the countries worldwide. Individuals with ASD struggles in daily life activities. Detection of autism with the help of medical tests is a tedious and very costly task. However, detection and care of ASD still remains unfamiliar due to inadequate awareness, knowledge among the society, limited number of diagnostic devices and limited verbal therapy services for ASD patients. This paper investigates and displays reviews of various machine learning approaches on extracting useful data associated with distinctive characteristics of ASD such as brain functioning, hyperactivitperactivity, language disability, etc. Current researches reveal that analysis of biological traits by employing machine learning techniques have helped in the progress of early detection of ASD. ABIDE dataset is very much explored for the research in ASD. Additionally, numerous studies for the advancement of tools are still in progression. The presented research work can remarkably aid future studies on machine learning for ASD.

关键词： Autism Medical treatment Machine learning Market research Medical tests Biology data mining

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Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation 26

Geo-teaser: Geo-temporal sequential embedding rank for point...

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26th International World Wide Web Conference, WWW 2017 Companion

作者： Zhao, Shenglin Zhao, Tong King, Irwin Lyu, Michael R. Shenzhen Key Laboratory of Rich Media Big Data Analytics Application Shenzhen Research Institute Chinese University of Hong Kong Nanshan District Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Shatin N.T. Hong Kong

ISBN: (纸本)9781450349147

Point-of-interest (POI) recommendation is an important application for location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. Previous studies show that modeling the sequential pattern of user check-ins is necessary for POI recommendation. Markov chain model, recurrent neural network, and the word2vec framework are used to model check-in sequences in previous work. However, all previous sequential models ignore the fact that check-in sequences on different days naturally exhibit the various temporal characteristics, for instance, "work" on weekday and "entertainment" on weekend. In this paper, we take this challenge and propose a Geo-Temporal sequential embedding rank (Geo-Teaser) model for POI recommendation. Inspired by the success of the word2vec framework to model the sequential contexts, we propose a temporal POI embedding model to learn POI representations under some particular temporal state. The temporal POI embedding model captures the contextual check-in information in sequences and the various temporal characteristics on different days as well. Furthermore, We propose a new way to incorporate the geographical influence into the pairwise preference ranking method through discriminating the unvisited POIs according to geographical information. Then we develop a geographically hierarchical pairwise preference ranking model. Finally, we propose a unified framework to recommend POIs combining these two models. To verify the effectiveness of our proposed method, we conduct experiments on two real-life datasets. Experimental results show that the Geo-Teaser model outperforms state-of-the-art models. Compared with the best baseline competitor, the Geo-Teaser model improves at least 20% on both datasets for all metrics. © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License.

关键词： Location based services

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Retraction notice to “Neural network-based optimization of hydrogen fuel production energy system with proton exchange electrolyzer supported nanomaterial” [Fuel 332 (2023) 125827]

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Fuel 2025年 391卷

作者： Tao Hai Kosar Hikmat Hama Aziz Jincheng Zhou Hayder A. Dhahad Kamal Sharma Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Teeba Ismail Kh Sadok Mehrez Anas Abdelrahman School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Department of Chemistry College of Science University of Sulaimani Qlyasan Street Kurdistan Region 46001 Iraq Department of Medical Laboratory of Science College of Health Sciences University of Human Development Sulaimaniyah Iraq Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan Duyun 558000 China Mechanical Engineering Department University of Technology Baghdad Iraq Institute of Engineering and Technology GLA University Mathura Uttar Pradesh 281406 India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Computer Engineering College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq Department of Mechanical Engineering College of Engineering at Al Kharj Prince Sattam bin Abdulaziz University 16273 Saudi Arabia Department of Mechanical Engineering University of Tunis El Manar ENIT BP 37 Le Belv’ed‘ere Tunis 1002 Tunisia Department of Chemical Engineering Faculty of Engineering & Technology Future University in Egypt New Cairo 11500 Tunisia

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Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Recognition Framework 1

Augmenting Cybersecurity in WSN: AI-Based Clone Attacks Reco...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Bhaskar, Seelam Ch Vijaya Kalyani, Gedela Al-Fatlawy, Ramy Alsalamy, Fatima Kurdi, Waleed Hadi Madhloom Al-Aboudy, H.M. Sujatha, G. MVSR Engineering College Department of IT Telangana Hyderabad India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Altoosi University College Department of Computer Science Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq Arunai Engineering College Department of ECE Tamil Nadu Tiruvannamalai India

ISBN: (纸本)9798350367003

Applications such as industrial automation, healthcare, and environmental monitoring need the use of wireless sensor networks (WSNs). However, due to their dispersed organizational makeup, they have become vulnerable to security risks, particularly clone assaults. To protect confidentiality, availability, and confidentiality, several attacks must be recognized and prevented. This project aims to offer an effective method for identifying and averting clone assaults. To identify cloned both nationally and internationally use a low-cost verification process. In this study, we offer a new adaptive sea-horse optimized light gradient boosting machine (ASHO-LGBM) technique for protecting the network against node identity duplicates. The ASHO approach is used in the ASHO-LGBM framework to improve the recognition accuracy of the light gradient boosting machine (LGBM) characteristics. The replications with the nodes intrusion detection (ID) are used to choose a most trustworthy communication mode. The procedure is intended to be implemented and used for gathering data through an internet component. Using a Python tool, the suggested technique is simulated and its delay, packet delivery ratio, packet drop, and energy are evaluated. When compared to other approaches, the study's results show that the ASHO-LGBM strategy's performance analysis achieves the highest accuracy rate. © 2024 IEEE.

关键词： Sensor nodes

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A Review and Bibliometric Analysis of the Current Studies for the 6G Networks

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computer Modeling in engineering & sciences 2024年第9期140卷 2165-2206页

作者： Qusay M.Salih Md.Arafatur Rahman Ahmad Firdaus Mohammed Rajih Jassim Hasan Kahtan Jasni Mohamad Zain Ahmed Hussein Ali Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)PekanPahang26600Malaysia Department of Medical Equipment Technology Engineering Al Salam University CollegeBaghdadIraq School of Mathematics and Computer Science University of WolverhamptonWolverhampton01902UK Department of Production Engineering and Metallurgy University of TechnologyBaghdadIraq Cardiff School of Technologies Cardiff Metropolitan UniversityCardiffCF52YBUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia Department Computer Science Al-Salam University CollegeBaghdadIraq

The race to develop the next generation of wireless networks,known as Sixth Generation(6G)wireless,which will be operational in 2030,has already *** realize its full potential over the next decade,6G will undoubtedly necessitate additional improvements that integrate existing solutions with cutting-edge ***,the studies about 6G are mainly limited and scattered,whereas no bibliometric study covers the 6G ***,this study aims to review,examine,and summarize existing studies and research activities in *** study has examined the Scopus database through a bibliometric analysis of more than 1,000 papers published between 2017 and ***,we applied the bibliometric analysis methods by including(1)document type,(2)subject area,(3)author,and(4)country of *** study’s results reflect the research 6G community’s trends,highlight important research challenges,and elucidate potential directions for future research in this interesting area.

关键词： 6G bibliometric wireless networks review

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Design of a Hybrid Brain-computer Interface and Virtual Reality System for Post-Stroke Rehabilitation

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IFAC-PapersOnLine 2020年第2期53卷 16010-16015页

作者： Mengjie Huang Yuting Zheng Jingjing Zhang Bin’ao Guo Chenyu Song Rui Yang Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool University Suzhou 215123 China Research Institute of Big Data Analytics Xi’an Jiaotong-Liverpool University Suzhou 215123 China

As one of common diseases among elderly, stroke often leads to motor impairment and even serious disability. Post-stroke rehabilitation is of great importance to restore the motor function and improve the life quality of stroke survivors. This study therefore sets out to propose a hybrid system based on brain-computer interface and virtual reality, which can provide various training programs including action observation, motor imagery and physical therapy for post-stroke patients with different motor control levels and training demands. The present work offers new insights into the way in which the conventional rehabilitation programs can be turned into innovative and interactive training experiences with advanced technologies to make optimal rehabilitation outcomes for stroke survivors.

关键词： Rehabilitation engineering Stroke Virtual reality Brain-computer interface Motor imagery

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Improving Power Allocation and Installation in WSN Using Novel Nature-Inspired Optimization for Cyber Security Applications 1

Improving Power Allocation and Installation in WSN Using Nov...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Balamurugan, K.S. Kalyani, Gedela Al-Fatlawy, Ramy Kurdi, Waleed Hadi Madhloom Alsalamy, Fatima Al-Aboudy, H.M. Manikandan, K. Karpaga Vinayaga College of Engineering and Technology Department of ECE Tamil Nadu Chengalpattu India Karpaga Vinayaga College of Engineering and Technology Big Data Analytics Tamil Nadu Chengalpattu India The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College DhiQar Department of Computer Techniques Engineering Iraq Sona College of Technology Department of Biomedical Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350367003

Wireless sensor network (WSN) installation and power allocation optimization for cybersecurity purposes are difficult tasks which include a well-thought-out method that strikes a balance between goals such as the energy efficiency, connectivity and cyber threat resistance while improving overall system performance and cybersecurity. A novel multi-task walrus optimization (MTWO) technique is offered in this study. The installation and power allocation problem (IPAP) with several jobs is established in this research. The suggested MTWO is used to split the IPAP into numerous scalar components, which are consequently grouped and treated by their desired goals. We evaluate the proposed MTWO approach using simulations and show that it works well in practice. The findings demonstrate that power distribution and installation for cybersecurity activities on WSNs can be improved by the MTWO. The superiority of the problem specific MTWO over the MOGA has been demonstrated by simulation outcome in several network cases, offering a wide range of excellent network designs to aid in the decision-maker selection. © 2024 IEEE.

关键词： Energy efficiency

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JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences through Multi-Task Learning

JH-Ranker: Enhancing Keigo Recognition in Japanese Sentences...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shih-Wei Guo Ya-Chun Wen Jui-Ling Yu Yao-Chung Fan Department of Computer Science and Engineering National Chung Hsing University Taichang Taiwan Department of Japanese Language and Literature Providence University Taichang Taiwan Department of Data Science and Big Data Analytics Providence University Taichang Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of Japanese language processing, the correct use and understanding of Keigo (honorific language) presents unique challenges due to its complex nature and context-dependent usage. This study introduces JH-Ranker, a novel multitask learning framework designed to enhance the recognition and understanding of Keigo in Japanese sentences. We are employing a multi-task learning approach by integrating Keigo Level, Contextual Field, and the Classification of Keigo Forms. This integration aims to foster a comprehensive understanding of Keigo usage from small data, leveraging the synergy between multiple tasks to achieve a deeper understanding with limited resources. Utilizing the BERT-base-japanese pre-trained model, JH-Ranker takes into account various Keigo Forms and contextual scenarios, going beyond mere syntactic analysis. Our methodology also emphasizes the importance of considering both the form and context of Keigo, addressing challenges such as label imbalance and overfitting in multi-label classification tasks. The experimental results demonstrate a significant improvement in Keigo recognition capabilities over existing baseline models, particularly in discerning the subtle nuances of Keigo Levels and Forms. This study contributes to the realm of natural language processing by presenting a refined and contextually sensitive method for Japanese Keigo. It demonstrates the language model’s adaptability and understanding in dealing with intricate honorific language scenarios.

关键词： Adaptation models Analytical models Neural networks Syntactics Multitasking Natural language processing Context modeling

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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

arXiv

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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