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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.

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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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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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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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Prospector Heads: Generalized Feature Attribution for Large Models & data

arXiv

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arXiv 2024年

作者： Machiraju, Gautam Derry, Alexander Desai, Arjun Guha, Neel Karimi, Amir-Hossein Zou, James Altman, Russ B. Ré, Christopher Mallick, Parag Department of Biomedical Data Science Stanford University United States Cartesia AI Department of Computer Science Stanford University United States Department of Electrical & Computer Engineering University of Waterloo Canada Department of Radiology Stanford University United States

Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ML models in scientific and biomedical domains. Current methods for feature attribution, which rely on "explaining" the predictions of end-to-end classifiers, suffer from imprecise feature localization and are inadequate for use with small sample sizes and high-dimensional datasets due to computational challenges. We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods that can be applied to any encoder and any data modality. Prospector heads generalize across modalities through experiments on sequences (text), images (pathology), and graphs (protein structures), outperforming baseline attribution methods by up to 26.3 points in mean localization AUPRC. We also demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data. Through their high performance, flexibility, and generalizability, prospectors provide a framework for improving trust and transparency for ML models in complex domains. © 2024, CC BY.

关键词： Classification (of information)

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Enhanced adaptive data rate strategies for energy-efficient Internet of Things communication in LoRaWAN

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International Journal of Communication Systems 2025年第2期38卷

作者： Ali Lodhi, Muhammad Wang, Lei Mahmood, Khalid Farhad, Arshad Chen, Jenhui Kumari, Saru School of Software Technology Dalian University of Technology Dalian China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Department of Computer Science FAST-National University of Computer and Emerging Sciences Islamabad Pakistan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Mathematics Chaudhary Charan Singh University Meerut India School of Chemical Engineering and Physical Sciences Lovely Professional University Jalandhar India

The long-range wide area network (LoRaWAN) is a standard for the Internet of Things (IoT) because it has low cost, long range, not energy-intensive, and capable of supporting massive end devices (EDs). The adaptive data rate (ADR) adjusts parameters at both EDs and the network server (NS). This includes modifying the transmission spreading factor (SF) and transmit power (TP) to minimize packet errors and optimize transmission performance at the NS. The ADR managed by NS aims to provide reliable and energy-efficient resources (e.g., SF and TP) to EDs by monitoring the packets received from the EDs. However, since the channel condition changes rapidly in LoRaWAN due to mobility, the existing ADR algorithm is unsuitable and results in a significant amount of packet loss and retransmissions causing an increase in energy consumption. In this paper, we enhance the ADR by introducing Kalman filter-based ADR (KF-ADR) and moving median-based ADR (Median-ADR), which estimate the optimal SNR by considering the mobility later used to assign the SF and TP to EDs. The simulation results showed that the proposed techniques outperform the legacy ADRs in terms of convergence period, energy consumption, and packet success ratio. © 2024 John Wiley & Sons Ltd.

关键词： adaptive data rate Internet of Things Kalman filter LoRa LoRaWAN mobility resource allocation

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ION-C: Integration of Overlapping Networks via Constraints

arXiv

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arXiv 2024年

作者： Nair, Praveen Bhandari, Payal Abavisani, Mohammadsajad Plis, Sergey Danks, David Department of Computer Science and Engineering University of California San Diego United States Department of Electrical and Computer Engineering Georgia Institute of Technology United States TReNDS Center Department of Computer Science Georgia State University United States Halicioglu Data Science Institute Department of Philosophy University of California San Diego United States

In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. [2008] presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input. © 2024, CC BY.

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IoT Attack Detection and Prevention Through Machine Learning System 2

IoT Attack Detection and Prevention Through Machine Learning...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Agrawal, Kanishk Poli, Pavan Kumar Reddy Gupta, Yash Juneja, Garv Bopaiah, Adhip Kishen Arani, Dinesh Ashok Cse Manglayatan University Department of Iet Uttar Pradesh Aligarh India Managing Information Technology Implementations Program Management for Fortune 6 Healthcare Company United States Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani India Maharaja Surajmal Institute of Technology Guru Gobind Singh Indraprastha University Department of Computer Science and Engineering Delhi India Trinity College Bangalore India Manglayatan University Uttar Pradesh Aligarh India

ISBN: (纸本)9798350369960

Attacks on web applications are constantly growing in both frequency and severity. Abundant data on the internet stimulates hackers to attempt different types of cyberattacks. Attack detection using conventional approaches and outdated data processing techniques has become outmoded as a result of this development. The purpose of this study is to investigate IoT attacks and discuss the efficient ML technique implementation strategies for restricting security risks. Among different security techniques, Machine learning (ML) systems demonstrated commendable feasibility in improving network and device security for the Internet of Things. The study with contextual research recognises and comprehends that modified 'Support Vector Machine (SVM)' as well as 'Random Forest (RF)' ML techniques showed optimal performance in IoT attack detection and prevention. © 2023 IEEE.

关键词： Anomaly detection and Cyber security Internet of Things Machine learning

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A Survey on Evolutionary Feature Selection in Multi-Label Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world problems, MLC has become a popular area of research. These datasets frequently contain irrelevant, redundant, or noisy features, highlighting the importance of feature selection in MLC tasks. As a result, numerous multi-label feature selection (MLFS) approaches have been introduced in the literature. Given their effective search capabilities, evolutionary computation (EC) techniques have been adopted to develop MLFS approaches. However, there is a notable absence of a comprehensive survey dedicated to EC-based MLFS approaches to MLC tasks. Although few attempts have been made to address this gap, they do not provide detailed discussions on EC-based approaches. They also tend to overlook the strengths and weaknesses of existing approaches, especially regarding search, optimization, and evaluation processes. This paper aims to fill this gap by presenting a comprehensive survey of EC-based MLFS approaches, focusing on the latest advancements, current challenges, and future directions. To be specific, we categorize EC-based MLFS approaches based on different criteria and provide detailed descriptions for each category. © 1997-2012 IEEE.

关键词： Evolutionary Computation Multi-Label Classification Multi-Label Feature Selection Multi-Objective Optimization

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