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IEEE Globecom Workshops

作者： Muhammad Adil Muhammad Attique Jian Wang Faisal Alrefaei Houbing Song Ahmed Farouk Computer Science & Engineering University at Buffalo New York USA Department of Software Sejong University Seoul Korea Department of Computer Science University of Tennessee Martin TN USA Electrical Engineering & Computer Science Embry Riddle Aeronautical Universit Florida USA Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt

ISBN: (纸本)9781665459761

Internet of Softwarized Things (IoST) is a promising and dynamic programmable technology that has the capability to interconnect sensor devices with an objective to share the accumulated data in the network without the intervention of human beings. Despite its advantages, this technology suffers from numerous security vulnerability threats, which can damage the trust of clients. Therefore, it is very important to familiarize the readers, students, experts, and industry stakeholders working in this domain with the existing counteraction schemes followed by potential security threats. For redressal of identified security threats, the literature presents various schemes, but somehow they are unable to provide fool-proof security infrastructure for these networks, because the attackers work restlessly to compromise the new security countermeasure schemes. To explore this discussion and evaluate the open security challenges with future research directions, in this paper, we present a survey regarding the most recent published literature from 2020 to 2022. Following this, we have covered the architectural and interoperability literature in the context of security threats and their countermeasure techniques to identify the weak aspects of them. Based on underscored problems, we have highlighted the unresolved security challenges to set a footstep for the future research work that could be useful for all stakeholders associated with IoST technology.

关键词： Industries Performance evaluation Privacy Conferences Network security Internet Security

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A Distributed Feature Selection Approach Over Hadoop for Accurate Lung Cancer Classification Based on Grasshopper Algorithm and Rough Sets

SSRN

引用

SSRN 2023年

作者： Song, Xu Zhou, Hongyu Teo, Brian Sheng Xian Abo-Alnadr, Ayman Hamed, Ahmed School of Computer and Information Engineering Anyang Normal University Anyang455000 China School of Software Engineering Anyang Normal University Anyang455000 China School of Graduate Studies Shah Alam40100 Malaysia Kafrelsheikh University Egypt Department of Computer Science Faculty of Computers and Information Damanhour University Damanhour22511 Egypt

In the specialized field of lung cancer research, precise feature selection has become paramount, especially given the extensive and intricate datasets available. Many of these datasets house a plethora of features, of which a substantial number may be redundant, leading to potential inaccuracies and increased computational demands. Although the Rough Set (RS) and Multigranular Rough Set (MGRS) models have demonstrated efficacy in feature selection, their computational complexities can be limiting. To address this, we introduce an innovative solution, integrating the MGRS with the Grasshopper Optimization Algorithm (GOA)—a metaheuristic technique derived from grasshopper foraging behaviors. To manage the expansive lung cancer data, we employ the Hadoop framework for streamlined distributed processing. By distributing the enhanced GOA tasks within Hadoop, we aspire to efficiently process large-scale datasets. The proposed algorithm's efficacy is assessed using dedicated lung cancer datasets, benchmarked via classifiers such as Random Forest and K-Nearest Neighbor. Preliminary results highlight the superior performance of our approach compared to prevalent metaheuristic strategies, with the MGRS model enhancing performance notably when employed as an objective function. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Impact of UAV Mobility on Physical Layer Security

Impact of UAV Mobility on Physical Layer Security

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Rukhsana Ruby Basem M. ElHalawany Kaishun Wu College of Computer Science and Software Engineering Shenzhen University China Faculty of Engineering at Shoubra Benha University Egypt

ISBN: (纸本)9781665406697

Mobility is one of the most fascinating features of unmanned aerial vehicles (UAVs) to support many critical applications (e.g., data dissemination) in emergency scenarios. Communication information in such applications could be confidential, and hence an effective security technique is required to mitigate the presence of an eavesdropper to some extent. On the other hand, blessed by the flexibility in the management and low overhead, physical layer security has received a significant attention in the recent years. To this end, we consider a communication system, in which a sensor-equipped mobile UAV hovers over a region to collect information and then disseminate this information to static a ground network entity in a confidential manner. Because of the popularity and practicality, the UAV is assumed to hover following the random way point (RWP) mobility model. We investigate the secrecy characteristics of the UAV under steady-state running state in terms of positive secrecy capacity probability and secrecy outage probability for the communication between the UAV and the receiver. Because of the intractable singleton analysis owing to the UAV mobility, we derive four separate closed form expressions of these performance metrics in four quadrants of conventional coordinate systems. We then investigate the secrecy performance of the UAV while considering the pause time of its RWP model. Furthermore, we propose two types of secrecy improvement strategies for the considered communication model. We strike a good trade-off between the secrecy improvement and transmit outage probability. Extensive simulations have been conducted to validate our theoretical analysis.

关键词： Analytical models Closed-form solutions Simulation Probability Physical layer security Autonomous aerial vehicles Power system reliability

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EPPR: Blockchain for educational record sharing and recommendation using the Ethereum platform

EPPR: Blockchain for educational record sharing and recommen...

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作者： Alkouz, Akram HajYasien, Ahmed Alarabeyyat, Abdulsalam Samara, Khalid Al-Saleh, Mohammed I. Computer Information Sciences Higher Colleges of Technology Abu Dhabi United Arab Emirates Software Engineering Department Faculty of Prince Abdullah Bin Ghazi of Information Technology Al-Balqa' Applied University As-Salt Jordan Department of Computer Science Jordan University of Science and Technology Al Ramtha Irbid Jordan

There has been a great deal of discussion of the challenges on privacy of Educational Professional Personal Record (EPPR). Therefore, it is required to reassess the current models, in which various parties generate, exchange and observe a huge amount of personal data with regard to EPPR. Ethereum blockchain has shown that trusted, auditable transactions are detectible using a decentralised network of nodes. In this paper, we propose a novel decentralised framework to manage EPPR using Ethereum blockchain. The framework provides the owner of the EPPR a comprehensive immutable log and accessibility to their educational records across the educational record editors and consumers. Furthermore, it provides a recommender engine to endorse skills and competencies to the education record owners and similar candidates for educational records editors and consumers. The aim of the proposed framework is to enable educational stakeholders to participate in the network as blockchain miners rewarded by pseudonymised data. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Blockchain

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Leaf Guard: Detecting the Diseases in Banana Leaf

Leaf Guard: Detecting the Diseases in Banana Leaf

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Advancements in Computing (ICAC), International Conference on

作者： K. Yathurshan S. Barathy G. Karini P. Sajitha Pradeep Abeygunawardhna Kaushalya Rajapakse Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Engineering| Electrical & Electronic Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

This research paper introduces “Leaf Guard,” a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, ‘Leaf Guard’ provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation.

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal)

Smart Farmer Education System for Disease Identification, Se...

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International Conference on Industrial and Information Systems, ICIIS

作者： Keshara Bandara Samitha Vidhanaarachchi A.A.E. Nanayakkara Kavindu Kulasinghe Sanka Rajapakshe Asela Wickramasinghe Kanishka Yapa Department Of Information Technology Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department Of Computer Science and software Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka Department of Soil Science Faculty of Agriculture University of Ruhuna Mapalana Kamburupitiya Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute Of Information Technology Malabe Sri Lanka

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed ‘AI-based mobile application system for Brinjal Diseases,’ which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections.

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Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases

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Informatics in Medicine Unlocked 2022年 32卷 101003页

作者： Al-Mustanjid, Md Mahmud, S. M. Hasan Akter, Farzana Rahman, Md Shazzadur Hossen, Md Sajid Rahman, Md Habibur Moni, Mohammad Ali Department of Software Engineering Faculty of Science and Information Technology Daffodil International University Dhaka 1207 Bangladesh Department of Computer Science American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computer Science & Engineering Faculty of Science and Information Technology Daffodil International University Dhaka 1207 Bangladesh Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Department of Computer Science and Engineering Pabna Science & Technology University Pabna 6600 Bangladesh

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19. © 2022 The Authors

关键词： Autoimmune diseases COVID-19 DEGs Drug repurposing Hub gene Protein-protein interaction network (PPIN) SARS-CoV-2 Transcriptomic analysis

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Stress Detection of Children With ASD Using Physiological Signals

Stress Detection of Children With ASD Using Physiological Si...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Sevgi Nur Bilgin Aktaş Pı nar Uluer Buket Coş kun Elif Toprak Duygun Erol Barkana Hatice Kö se Tatjana Zorcec Ben Robins Agnieszka Landowska Department of AI and Data Engineering Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Department of Computer Engineering Faculty of Technology and Engineering Galatasaray University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering Yeditepe University Istanbul Turkey University Children&#x2019 s Hospital Skopje North Macedonia School of Computer Science University of Hertfordshire Hatfield UK Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

This paper proposes a physiological signal-based stress detection approach for children with autism spectrum disorder (ASD) to be used in social and assistive robot intervention. Electrodermal activity (EDA) and blood volume pulse (BVP) signals are collected with an E4 smart wristband from children with ASD in different countries. The peak count and signal amplitude features are derived from EDA signal and used in order to detect the stress of children based on the previously provided reference baselines. Furthermore, a comparison has been made with the stress values determined using low frequency (LF) and high frequency (HF) values extracted from BVP signal.

关键词： Pediatrics Autism Social robots Signal processing Feature extraction Assistive robots Physiology

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TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates

TRIPORA: Intelligent Machine Learning Solution for Sri Lanka...

引用

Advancements in Computing (ICAC), International Conference on

作者： T.R Legrand K.M.R.A.I Bandara J.A.D Stefania Crishani L.W.P Uvindu Nelum Amarasena Dharshana Kasthurirathna Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Sri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.

关键词： Machine learning Aging Recommender systems Faces

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Open-Vocabulary Affordance Detection in 3D Point Clouds

arXiv

引用

arXiv 2023年

作者： Nguyen, Toan Vu, Minh Nhat Vuong, An Nguyen, Dzung Vo, Thieu Le, Ngan Nguyen, Anh FPT Software AI Center Viet Nam VNUHCM-University of Science Ho Chi Minh City Viet Nam TU Wien Vienna Austria Vienna Austria Faculty of Mathematics and Statistics Ton Duc Thang University Ho Chi Minh City Viet Nam Department of Computer Science & Computer Engineering University of Arkansas United States Department of Computer Science University of Liverpool United Kingdom

Affordance detection is a challenging problem with a wide variety of robotic applications. Traditional affordance detection methods are limited to a predefined set of affordance labels, hence potentially restricting the adaptability of intelligent robots in complex and dynamic environments. In this paper, we present the Open-Vocabulary Affordance Detection (OpenAD) method, which is capable of detecting an unbounded number of affordances in 3D point clouds. By simultaneously learning the affordance text and the point feature, OpenAD successfully exploits the semantic relationships between affordances. Therefore, our proposed method enables zero-shot detection and can be able to detect previously unseen affordances without a single annotation example. Intensive experimental results show that OpenAD works effectively on a wide range of affordance detection setups and outperforms other baselines by a large margin. Additionally, we demonstrate the practicality of the proposed OpenAD in real-world robotic applications with a fast inference speed. Our project is available at https://***. © 2023, CC0.

关键词： Intelligent robots

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