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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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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

arXiv

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arXiv 2023年

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level *** Codes 68T07 © 2023, CC BY-NC-ND.

关键词： eHealth

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Android malware category and family detection and identification using machine learning

arXiv

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arXiv 2021年

作者： El Fiky, Ahmed Hashem El Shenawy, Ayman Madkour, Mohamed Ashraf Systems and Computer Engineering Dept. Faculty of Engineering Al-Azhar University Cairo Egypt Software Engineering and Information Technology Faculty of Engineering and Technology Egyptian Chinese University Cairo Egypt

Android malware is one of the most dangerous threats on the internet, and it's been on the rise for several years. Despite significant efforts in detecting and classifying android malware from innocuous android applications, there is still a long way to go. As a result, there is a need to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Each Android malware family and category has a distinct objective. As a result, it has impacted every corporate area, including healthcare, banking, transportation, government, and e-commerce. In this paper, we presented two machine-learning approaches for Dynamic Analysis of Android Malware: one for detecting and identifying Android Malware Categories and the other for detecting and identifying Android Malware Families, which was accomplished by analyzing a massive malware dataset with 14 prominent malware categories and 180 prominent malware families of CCCS-CICAndMal2020 dataset on Dynamic Layers. Our approach achieves in Android Malware Category detection more than 96 % accurate and achieves in Android Malware Family detection more than 99% accurate. Our approach provides a method for high-accuracy Dynamic Analysis of Android Malware while also shortening the time required to analyze smartphone malware. © 2021, CC BY-NC-ND.

关键词： Malware

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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Combinatorial Test Suite Generation Strategy Using Enhanced Sine Cosine Algorithm 5th

Combinatorial Test Suite Generation Strategy Using Enhanced ...

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Zamli, Kamal Z. Din, Fakhrud Nasser, Abdullah B. Alsewari, AbdulRahman Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang KuantanPahang26300 Malaysia Department of Computer Science & IT University of Malakand Dir Lower ChakdaraKPK Pakistan

ISBN: (纸本)9789811523168

Owing to its simplicity and having no control parameters, the Sine Cosine Algorithm (SCA) has attracted much attention among researchers. Although useful, the SCA algorithm adopts a linear magnitude update to determine its sine or cosine position updates. In the actual searching process, the magnitude update is rarely linear. In fact, the magnitude update is also non-exponential and is highly dependent on the problem domain and its search topology. For this reason, our work proposes a combination of linear and exponential magnitude update for the search displacement. In doing so, we adopt the combinatorial testing problem as our case study. Combinatorial testing strategies generate test data which cover all required interactions among parameter values of a system-under-test in order to explore interaction faults. Our evaluation gives promising results on the improved performance over the original SCA algorithm. As far as test data generation time is concerned, the enhanced SCA outperformed all its counterparts, whereas its results in terms of test suite sizes are comparable to other parameter free meta-heuristic algorithms. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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Smart Irrigation Management System

Smart Irrigation Management System

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Bassem Sellami Abdelaziz El Fazziki Mohamed Taha Bennani Sadok Ben Yahia Dept of Computer Sciences Faculty of Sciences University of Tunis El Manar Tunis Tunisia Dept of Computer Science Cadi Ayyad University Computing Systems Engineering Laboratory Marrakech Morocco Department of Software Science Tallinn University of Technology Tallinn Estonia

The significant problem in the area of sustainable development is the effective management of water resources. There are a number of approaches for resolving this issue in the literature, particularly in the agricultural sector, which serves as the primary consumer through irrigation. As a result, irrigation management is a crucial and cutting-edge discipline that has been the focus of several research and study projects to address the various activities, behaviors, and conflicts between the various users. This paper introduces an intelligent irrigation system based on smart sensors that can be used sparingly and affordably to monitor farms. By incorporating some connected electronic devices and other useful tools frequently used in the field of IoT, it calculates the water requirement of each farm based on the water loss caused by the process of evapotranspiration. Data gathered from a number of sensors placed across the plantation farm are used to determine the amount of water needed. It may be utilized by farmer organizations whose endowments and irrigation planning are determined according to the requirement and quantity of water available in the rural municipality. This project focuses on smart irrigation based on IoT. Each node in the system functions as an IoT device and incorporates a microprocessor with the integration of sensors, actuators, and valve modules. An irrigation management system simplifies the operation of each node and configuration settings for irrigation allow for the direct monitoring of environmental factors. The Penman model is used to get the daily evapotranspiration baseline and is used to calculate the quantity of water needed for irrigation. The suggested irrigation approach is anticipated to help reduce water usage and distribute it fairly without affecting crop productivity compared to the traditional irrigation system.

关键词： Irrigation Crops Organizations Valves Planning Internet of Things Intelligent sensors

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Describing Brachiopods Shape Based on Fusion of Curvature and Fourier Descriptors 12th

Describing Brachiopods Shape Based on Fusion of Curvature an...

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12th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th International Conference on Information Assurance and Security, IAS 2020

作者： Khouya, Youssef Ait Oussous, Mohammed Ait Ghorbel, Faouzi Software Engineering and Information Systems Engineering Team Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco Qualifying High School Ibn Albana Ministry of National Education Rabat Morocco GRIFT Research Group CRISTAL Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia

ISBN: (纸本)9783030736880

The fossils classification has a great importance in palaeontological studies. They make it possible to understand the biodiversity in its morphological dimension and they show the morphological transformations suffered during the biological evolution. In this paper, we propose a descriptor for fossils classification and particularly Brachiopods. The proposed descriptor is a fusion between the curvature and Fourier descriptors. The curvature properties provide an apparently powerful cue to the underlying structure of the curve and captures completely the structure of planar curve. In addition, it is stable and complete. Fourier descriptors are powerful features for the recognition of two-dimensional connected shapes and is supported by the well-developed and well-understood Fourier theory. We used the Fourier descriptors proposed by Ghorbel, this set of invariants is also stable and complete. Experimental results are performed to illustrate the performance of the proposed descriptor. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biodiversity

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A Method of Deep Reinforcement Learning for Simulation of Autonomous Vehicle Control 16

A Method of Deep Reinforcement Learning for Simulation of Au...

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16th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2021

作者： Huynh, Anh T. Nguyen, Ba-Tung Nguyen, Hoai-Thu Vu, Sang Nguyen, Hien D. Faculty of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam Faculty of Information Systems University of Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789897585081

Nowadays autonomous driving is expected to revolutionize the transportation sector. Carmakers, researchers, and administrators have been working on this field for years and significant progress has been made. However, the doubts and challenges to overcome are still huge, regarding not only complex technologies but also human awareness, culture, current traffic infrastructure. In terms of technical perspective, the accurate detection of obstacles, avoiding adjacent obstacles, and automatic navigation through the environment are some of the difficult problems. In this paper, an approach for solving those problems is proposed by using of Policy Gradient to control a simulated car via reinforcement learning. The proposed method is worked effectively to train an agent to control the simulated car in Unity ML-agents Highway, which is a simulating environment. This environment is chosen from some criteria of an environment simulating autonomous vehicle. The testing of the proposed method got positive results. Beside the average speed was well, the agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing. Copyright © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

关键词： Reinforcement learning

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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