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3rd International Conference on Information Science and System, ICISS 2020

作者： Anjum, Shaik Shabana Noor, Rafidah Md Ahmedy, Ismail Khamis, Norazlina Binti Anisi, Mohammad Hossein Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

ISBN: (纸本)9781450377256

The technological advancements of the Internet of Things (IoT) in the recent past have facilitated immense progress towards mitigation of environmental pollution through smart transportation systems and solutions. In particular, communication to the commuters about the traffic ahead or occurrences of congestion has been envisioned to play a major role in outsmarting traffic through mobile applications giving rise to the emergence of the Internet of Mobile Things (IoMT). However, the existing mobile applications that serve as traffic reporting solutions still face major issues such as fixed route suggestions, longer delays during busy hours or emergencies, inefficient prompting of road accidents and heavy traffic en route to a particular destination. This research aims at providing solutions for notifying the commuters with updates on the traffic based upon the Air Quality Index (AQI) of the routes towards the destination and also about the approach of emergency vehicles. The cross-platform mobile application in this way enables the user to opt for a route with good air quality so that the more congested routes are avoided thereby mitigating the air pollution induced by road traffic. The experimental testing and validation of the proposed methodology are applied for areas belonging to Greater Kuala Lumpur. The various timings divided according to peak and non-peak hours are experimentally tested for analyzing the parameters of traffic usage and pattern through the mobile application. The outcome of the experiments has showed that when traffic flow is modelled and governed through vehicular emissions and concentrations of air pollutants, nearly 75% of the congested traffic is reduced thereby, giving rise to pollution-free environment as well as mitigation of urban heat island (UHI) effect that is formed through vehicular heat generation and difference in temperatures. On the other hand, the approach of emergency vehicles also prompts the commuters to avoid panic. © 20

关键词： Traffic congestion

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Total controllability analysis discovers explainable drugs for Covid-19 treatment

arXiv

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arXiv 2022年

作者： Wei, Xinru Pan, Chunyu Zhang, Xizhe Zhang, Weixiong Early Intervention Unit Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Jiangsu Nanjing210029 China School of Biomedical Engineering and Informatics Nanjing Medical University Jiangsu Nanjing210001 China School of Computer Science and Engineering Northeastern University Shenyang China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Department of Genetics Washington University in St. Louis St. LouisMO United States

Network medicine has been actively pursued for drug repurposing, particularly lately for combating the Covid-19 pandemic. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect, rather than control, the cell from viral infections, we extended this theory to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous network of human protein-protein interactions and interactions between human and SARS-Cov-2 proteins. We identified 65 druggable control hubs enriched with antiviral functions and used them to prioritize drugs into four major categories: antiviral and anti-inflammatory agents, drugs on central nerve systems, dietary supplements, and hormones that help boost immunity. The functions of these druggable control hubs and their corresponding drugs validated our new method. One of the identified drugs is Fostamatinib, which was originally approved for chronic immune thrombocytopenia and is currently in a clinical trial for treating Covid-19. As shown in a preclinical trial, Fostamatinib can lower mortality, shorten the length of ICU stay, and reduce the disease severity of hospitalized Covid-19 patients. Fostamatinib targets 10 control hubs, 9 of which are kinases playing key roles in cell proliferation, differentiation, and programmed death. One of these kinases is RIPK1 which directly interacts with the RNA-dependent RNA polymerase of SARS-CoV-2. The functions and biological processes that the druggable control hubs were involved with have not only revealed the diverse cellular responses during SARS-CoV-2 infection but provided deep insight into the therapeutic mechanisms of the drugs for Covid-19 therapy, making the new approach

关键词： Enzymes

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Artificial Intelligence Approach for Name Classification in Information-Centric Networking-based Internet of Things 2

Artificial Intelligence Approach for Name Classification in ...

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2nd International Conference on Sustainable Engineering and Creative computing, ICSECC 2020

作者： Safitri, Cutifa Mandala, Rila Nguyen, Quang Ngoc Sato, Takuro Faculty of Computing President University Bekasi Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Communications and Computer Engineering Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781728175881

Content management has continuously been among the most challenging problems, especially on the Internet of Things (IoT), where devices are set to be "hungry" for content. In this context, Information-Centric Networking (ICN), a promising Future Internet Architecture, can facilitate the IoT requirements of continuous and long-lasting connectivity, which has burdened the IP-based network system. In ICN, a content naming structure is composed to direct content requests to the nearest content provider with the built-in in-network caching function, storing the content. For a successful implementation of ICN-based IoT, an intelligent algorithm approach for IoT-based ICN implementation aiming to improve content management is proposed in this study. We establish a hybrid ICN with the most suitable machine learning algorithms satisfying the requirements to realize a feasible IoT technology. The selected algorithms from Supervised Learning, Unsupervised Learning, and Reinforcement Learning are evaluated before being chosen as the content forwarding process. The numerical findings show the superiority of the Extended Learning Classifier System under Reinforcement Learning’s scheme compared to the other algorithms. © 2020 IEEE.

关键词： Internet of things

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Multi-step predictions for adaptive sampling using proximal ADMM

TechRxiv

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TechRxiv 2021年

作者： Le, Viet-Anh Nguyen, Linh Nghiem, Truong X. The School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States The School of Engineering Information Technology and Physical Sciences Federation University Australia Churchill VIC3842 Australia Northern Arizona University United States

This paper presents a novel approach, using multi-step predictions, to the adaptive sampling problem for efficient monitoring of environmental spatial phenomena in a resource- and obstacle-constrained mobile sensor network. We employ a Gaussian process (GP) to represent the spatial field of interest, which can then be used to predict the field at unmeasured locations. The adaptive sampling problem aims to drive the mobile sensors to optimally navigate the environment where the sensors adaptively take measurements of the spatial phenomena at each sampling step. To this end, an optimal sampling criterion based on conditional entropy is proposed, which aims to minimize the prediction uncertainty of the GP model. By predicting the measurements the mobile sensors potentially take in a finite horizon of multiple future sampling steps and exploiting the chain rule of the conditional entropy, a multi-step-ahead adaptive sampling optimization problem is formulated. The objective of the optimization problem is to find the optimal sampling paths for the mobile sensors in multiple sampling steps ahead. Compared with the single-step-ahead approach typically adopted in the literature, our approach provides better navigation, deployment and data collection with more informative sensor readings. However, this optimization problem is non-convex and highly complex. Even more challenging is that it is a mixed-integer problem, due to collision avoidance constraints. We propose to employ the proximal alternating direction method of multipliers algorithm to efficiently solve this problem. More importantly, the solution obtained by the proposed algorithm is theoretically guaranteed to converge to a stationary value. The effectiveness of our proposed approach was extensively validated by simulation using a real-world dataset, which showed highly promising results. © 2021, CC BY.

关键词： Sensor networks

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Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System 27

Optimizing Forest Fire Prevention: Intelligent Scheduling Al...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Jemmali, Mahdi Melhim, Loai Kayed B. Boulila, Wadii Amdouni, Hajer Alharbi, Mafawez T. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates MARS Laboratory University of Sousse Sousse Tunisia Department of Computer Science Higher Institute of Computer Science and Mathematics Monastir University Monastir5000 Tunisia Department of Health Information Management and Technology College of Applied Medical Sciences University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Ecole Supérieure des Sciences Economiques et Commerciales de Tunis Université de Tunis Tunisia Unit of Scientific Research Applied College Qassim University Buraydah51452 Saudi Arabia

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm RID achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： algorithms drone fire Forest monitoring

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Quantifying knowledge synchronisation in the 21st century

arXiv

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arXiv 2022年

作者： Yoon, Jisung Park, Jinseo Yun, Jinhyuk Jung, Woo-Sung Department Of Industrial And Management Engineering Pohang University Of Science And Technology Pohang37673 Korea Republic of Center For Complex Networks And Systems Research Luddy School Of Informatics Computing And Engineering Indiana University BloomingtonIN47408 United States Center For Global R&D Data Analysis Korea Institute Of Science And Technology Information Seoul02456 Korea Republic of School Of AI Convergence Soongsil University Seoul06978 Korea Republic of Department Of Physics Pohang University Of Science And Technology Pohang37673 Korea Republic of

Humans acquire and accumulate knowledge through language usage and eagerly exchange their knowledge for advancement. Although geographical barriers had previously limited communication, the emergence of information technology has opened new avenues for knowledge exchange. However, it is unclear which communication pathway is dominant in the 21st century. Here, we explore the dominant path of knowledge diffusion in the 21st century using Wikipedia, the largest communal dataset. We evaluate the similarity of shared knowledge between population groups, distinguished based on their language usage. When population groups are more engaged with each other, their knowledge structure is more similar, where engagement is indicated by socioeconomic connections, such as cultural, linguistic, and historical features. Moreover, geographical proximity is no longer a critical requirement for knowledge dissemination. Furthermore, we integrate our data into a mechanistic model to better understand the underlying mechanism and suggest that the knowledge "Silk Road" of the 21st century is based online. © 2022, CC BY.

关键词： Knowledge management

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Interplay of Machine Learning and Software Engineering for Quality Estimations

Interplay of Machine Learning and Software Engineering for Q...

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2020 IEEE International Conference on Communications, computing, Cybersecurity, and informatics, CCCI 2020

作者： Abubakar, Hamza Obaidat, M.S. Gupta, Aaryan Bhattacharya, Pronaya Tanwar, Sudeep Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad Gujarat India College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan University of Science and Technology Beijing Beijing10008 China

ISBN: (纸本)9781728120355

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of DevOps flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions. © 2020 IEEE.

关键词： Machine learning

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Pairing optimization via statistics: Algebraic structure in pairing problems and its application to performance enhancement

arXiv

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arXiv 2022年

作者： Fujita, Naoki Röhm, André Mihana, Takatomo Horisaki, Ryoichi Li, Aohan Hasegawa, Mikio Naruse, Makoto Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo Bunkyo-ku Hongo Tokyo113-8656 Japan Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu-shi Tokyo182-8585 Japan Department of Electrical Engineering Graduate School of Engineering Tokyo University of Science 6-3-1 Niijuku Katsushikaku Tokyo125-8585 Japan

Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. Copyright © 2022, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Designingfrom the rural

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Interactions 2019年第4期26卷 37-41页

作者： Hardy, Jean Phelan, Chanda Vigil-Hayes, Morgan Makoto Su, Norman Wyche, Susan Sengers, Phoebe Cornell University United States University of Michigan School of Information United States School of Informatics Computing and Cyber Systems Northern Arizona University United States School of Informatics Computing and Engineering Indiana University Bloomington United States Department of Media and Information Michigan State University United States Cornell in information science and science and technology United States

Human-computer interaction (HCI) is not alone in overlooking the rural. Though 45 percent of the world's population - over 3 billion people - lives in rural areas contemporary economic development policies marginalize rural residents. Rural areas' technological needs require solutions that are not readily apparent or possible from an urban perspective. Urban hand-me-down solutions are not enough. There is no single location for the rural in computing. Rurality is diverse: It is in almost every country on Earth and so resists easy definition. Rural computing is thus not a niche area but rather offers exciting opportunities that benefit design for all.

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