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arXiv 2024年

作者： Nakashima, Yuto Köppl, Dominik Funakoshi, Mitsuru Inenaga, Shunsuke Bannai, Hideo Department of Informatics Kyushu University Japan Department of Computer Science and Engineering University of Yamanashi Japan NTT Communication Science Laboratories M&D Data Science Center Tokyo Medical and Dental University Japan

We investigate the compression sensitivity [Akagi et al., 2023] of lex-parse [Navarro et al., 2021] for two operations: (1) single character edit and (2) modification of the alphabet ordering, and give tight upper and lower bounds for both operations. For both lower bounds, we use the family of Fibonacci words. For the bounds on edit operations, our analysis makes heavy use of properties of the Lyndon factorization of Fibonacci words to characterize the structure of lex-parse. © 2024, CC BY.

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QoE Enhancement of Multi-View Video Transmission Over ICN/CCN by Means of Cache Control Method

QoE Enhancement of Multi-View Video Transmission Over ICN/CC...

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IEEE Games Media Entertainment (GEM)

作者： Yoshiyuki Takada Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798350374537

ISBN: (纸本)9798350374544

This paper considers multi-view video and audio transmission on ICN (Information-Centric Networking)/CCN (Content-Centric Networking). Routers in ICN/CCN can cache content. Besides, the capacity of routers' caches is finite, so various cache control schemes have been proposed to improve cache efficiency. This paper presents and evaluates a new and suitable control scheme for multi-view video and audio transmission. For this purpose, we construct a network environment with Cefore. We assess application-level QoS (Quality of Service) and QoE (Quality of Experience). We then show the effectiveness of the proposed control scheme.

关键词： Information-centric networking Entertainment industry Quality of service Media Quality of experience Frequency control

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3D Based CNN for Improved Segmentation from the Hyperspectral Images

3D Based CNN for Improved Segmentation from the Hyperspectra...

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computer science and Emerging Technologies (CSET), International Conference on

作者： Narmadha T Arockia Raj A P Saravanan A Suresh Kumar Ramesh Sekaran Gowri Shankar J School of Computer Science and Engineering Jain Deemed to be University Bengaluru Department of Computer Science and Engineering - Data Science Madanapalle Institute of Technology and Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets.

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Fuzzy Cognitive Map and Deep-Seg Net for Enhancing Skin Cancer Segmentation and Detection 8th

Fuzzy Cognitive Map and Deep-Seg Net for Enhancing Skin Canc...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Kashid, Shraddha S. Mahalle, Parikshit N. Sule, Bipin Patil, Sambhaji A. Chitale, Swati S. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Electronic and Telecommunication PGM College of Engineering Wagholi SPPU Pune India Pune India

ISBN: (纸本)9789819766772

Global health challenges such as skin cancer, which arises from uncontrolled cell growth, pose a significant threat. The most prevalent form of skin cancer manifests in cells throughout the body, notably in the surface layer. Because of its widespread nature, early detection is urgently needed. An innovative approach is developed to segment and detect skin cancer by integrating Fuzzy Cognitive Maps (FCM) with Deep-Seg Nets. Adaptive bilateral filtering, preprocessing, and optimizing input skin cancer images are the first steps. In the following step, DeepSegNet, a hybrid of DeepJoint and SegNet, is used to segment the images. The segmented image is then enhanced using enhancement techniques, followed by extraction of diverse features such as texture, statistical attributes, Local Neighborhood Difference Patterns (LNDPs), and a novel contribution, a discrete wavelet transform (DWT)-based Local Directional Pattern. In skin cancer detection, FCM shows noteworthy proficiency. As a result of the evaluation of the proposed methodology, DeepSegNet scored 0.920 for dice coefficient, while the DeepSegNet+FCM model scored 97% accurate, 93% negative predictive value, 94% positive predictive value, 98% sensitivity, and 96% specificity. The combination of FCM and Deep-Seg Net offers a potential path toward more accurate and efficient skin cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adaptive filtering

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and Electrical Engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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FogMedX-Transform: A Transformer-Based Task Interoperability Framework for Energy-Efficient Fog-Enabled IoMT

FogMedX-Transform: A Transformer-Based Task Interoperability...

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International Conference on Inventive Computation Technologies (ICICT)

作者： R Jayakarthik Sajja Suneel Jayendra Gopal Thatipudi V. Jaganraja Minesh Vohra Gopinath D Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science University of North Texas Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Mechanical Engineering Lovely Professional University Phagwara Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transformer-based task interoperability framework purposebuilt for energy-efficient fog-enabled IoMT systems. A customized Transformer design utilizing multi-head selfattention allows the framework to deliver timely critical healthcare data processing alongside optimized energy usage. This model demonstrated a 98.7% task interoperability success rate coupled with a 97.5% anomaly detection accuracy that exceeded results from conventional models based in fog environments and the cloud. The framework improved performance through latency reduction between 50% and 70% and energy consumption minimization reached between 25% to 40% thus proving its effectiveness in resource administration. The scalability tests proved steady system functionality when dealing with different device quantities and built-in security features blocked data breaches and had a false positive rate of 2.3% during testing. FogMedX-Transform demonstrates the capability for healthcare transformation through its union of fog computing and deep learning technology integration according to test results.

关键词： Deep learning Performance evaluation Internet of Medical Things data breach Transformers data processing Energy efficiency Real-time systems Interoperability Anomaly detection

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Multi-agent Scheduling for Shared Manufacturing Systems

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Procedia computer science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

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AI-Powered Real-Time Obstacle Detection for UAVs using Blockchain

AI-Powered Real-Time Obstacle Detection for UAVs using Block...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Aparna Kumari Patel Ansh Alkeshbhai Shah Akshat Nimesh Safaa Najah Saud Al-Humairi Prasun Kumar Sunil Kumar Prakriti Lohumi Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Faculty of Engineering Science and Technology Adani University Ahmedabad India Faculty of Information Sciences and Engineering Management and Science University Malaysia Department of Biotechnology SSET Sharda University Greater Noida U.P. India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India Department of Artificial Intelligence and Data Science Bhagwan Parshuram Institute of Technology New Delhi Delhi India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work introduces an innovative AI-based solution for obstacle detection in UAVs, leveraging deep learning techniques to enhance precision and environmental awareness. The system’s architecture involves multiple layers, where UAVs first capture high-resolution images that undergo a processing pipeline including data pre-processing, augmentation, and labeling. A key element of this process is the use of Convolutional Neural Networks (CNNs) to train models capable of identifying obstacles across various terrains. To ensure the integrity and security of the data, especially in complex multi-UAV systems, blockchain technology is integrated. Utilizing Distributed Hash Tables (DHTs) and the Interplanetary File System (IPFS), this decentralized system creates a content-addressable database to store and authenticate unalterable records. Experimental analysis demonstrates that this system offers high accuracy in real-time obstacle detection, minimizing false positives and improving UAV safety.

关键词： Deep learning Autonomous aerial vehicles Real-time systems Blockchains Safety Reliability InterPlanetary File System Convolutional neural networks Artificial intelligence Drones

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A Survey: Performance-aware Depression Detection 10

A Survey: Performance-aware Depression Detection

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Jadhav, Ganesh Babar, Sachin Mahalle, Parikshit Smt. Kashibai Navale College of Engineering Vadgaon SPPU-Pune Department of Computer Engineering Pune India Sinhgad Institute of Technology Lonavala SPPU-Pune Department of Computer Engineering Pune India Vishwakarma Institute of Information Technology SPPU-Pune Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9789380544472

Depression is the most common health problem in the world. According to the World Health Organization (WHO), more than 350 million people are affected by it. There are many causes of depression, like business down and down, social life, economic life, and relationships. Depression is one of the leading causes of mental illness in humans and also affects an individual's thoughts, behavior, emotions and well-being. This survey is useful to the clinical understanding of mental health for patients. The main goal of the survey was to study the different learning techniques to find out the research gap. In this survey we studied the different existing research work with their systems, and different types of datasets (Image data, Audio data and textual data). The final results are based on different parameters like accuracy, precision, and recall etc. This paper explores the learning techniques to learn the different learning techniques for the depression detection system. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Diseases

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