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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Hassan, Rosilah Murad, Dina Fitria Wahi, Wahiza Wijanarko, Bambang Dwi Asmak Ismail, Nurul Halimatul Awwad, Samer A. B. Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Selangor Bangi Malaysia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information System Riyadh Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Due to the Covid-19 epidemic, the education system worldwide has changed profoundly. This has caused many lecturers to transform face-to-face teaching and learning to online learning. In this regard, this study focuses on students' experiences in dealing with online learning. A survey method using a questionnaire is used to gather data from students, and 46 respondents answered the survey. The survey is divided into three sections, which comprise of Online Learning (Assigning Tasks), Online Learning (Quizzes), and Online Learning (Online Meetings). The results showed that the online learning application that was mostly used for assigned tasks was Google Classroom, which was 78.3%. The online learning application that is mostly used for quizzes was Kahoot, 97.8% and for online meetings was Zoom, 63.2%. Overall, most respondents, 45.7% chose a combination of online learning and face-to-face classes. © 2022 IEEE.

关键词： Surveys

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RoadFormer: Duplex Transformer for RGB-Normal Semantic Road Scene Parsing

arXiv

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arXiv 2023年

作者： Li, Jiahang Zhang, Yikang Yun, Peng Zhou, Guangliang Chen, Qijun Fan, Rui The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at ***/RoadFormer. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Adaptive Strategy Templates Using Deep Reinforcement Learning for Multi-Issue Bilateral Negotiation

SSRN

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SSRN 2023年

作者： Bagga, Pallavi Paoletti, Nicola Stathis, Kostas Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Informatics King’s College London University of London United Kingdom Department of Computer Science Royal Holloway University of London United Kingdom

We introduce a novel negotiation model enabling an agent to learn a heuristic strategy from a given set of predefined tactics, forming a "strategy template." This template includes (a) choice parameters determining whether a tactic should be included in the strategy;(b) time parameters determining when should a tactic be triggered;and (c) attribute-value parameters determining values that constraint acceptance or bidding. The model accelerates learning through pre-training with teacher strategies and online training in diverse settings, yielding adaptive, non-pre-programmed, and transferable strategies. It handles user preference uncertainty, deriving a user model approximating partially specified preferences at negotiation start. A proof-of-concept prototype is developed using an actor-critic architecture based on deep reinforcement learning. Stochastic search derives the user model to handle preference uncertainty. A combination of multi-objective optimization and multi- criteria decision-making methods is used as one of the bidding tactics to generate near Pareto-optimal bids during negotiation. Extensive experimental evaluation demonstrates our model outperforms state-of-the-art in terms of both individual and social-welfare utilities. Combining learning from teacher strategies and different choice, time and attribute value parameters enables experience transfer across domains. This provides a significant advantage when confronting unfamiliar opponents in previously unseen negotiation scenarios. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Integrated E-Learning Fuzzy Expert System for Acute Pharyngitis Diagnosis 6

An Integrated E-Learning Fuzzy Expert System for Acute Phary...

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6th International Conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2022

作者： Istiadi Sulistiarini, Emma Budi Joegijantoro, Rudy Suksmawati, Affi Nizar Nugroho, Kuncahyo Setyo Akbar, Ismail Widyagama University of Malang Department of Informatics Engineering Malang Indonesia Widyagama University of Malang Department of Industrial Engineering Malang Indonesia Widyagama Husada College of Health Department of Environmental Health Science Malang Indonesia Gadjah Mada University Faculty of Mathematics and Natural Sciences Yogyakarta Indonesia Brawijaya University Faculty of Computer Science Malang Indonesia Maulana Malik Ibrahim State Islamic University Faculty of Science and Technology Malang Indonesia

ISBN: (纸本)9798350399615

A sore throat, or acute pharyngitis, is an inflammatory disease caused by viral and bacterial infections. Clinically, the causes of acute viral and bacterial pharyngitis are difficult to distinguish. In addition, there are differences in treatment for viral and bacterial acute pharyngitis. This study integrates expert and e-learning systems to diagnose acute pharyngitis. The Fuzzy Mamdani method is used for the disease diagnosis process in the expert system. System integration aims to optimize the database of disease diagnosis history results in fuzzy expert systems. Thus, the results of the history of disease diagnosis can be generated into test questions that will be retested on students. The results of testing the fuzzy expert system on 25 samples give it an accuracy of 84% The test results are strongly influenced by establishing a rule base between the premise and the consequent that is consistent and interrelated. © 2022 IEEE.

关键词： Bacterial Pharyngitis E-Learning System Expert System Mamdani Fuzzy Inference System Viral Pharyngitis

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Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Comparative Analysis of Various Models for Potato Leaf Disea...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： B. Krishnakumar K. Kousalya K. V. Indhu Prakash S. Jhansi Ida B. Ravichandra Rajeshkumar G Dept of CSE Kongu Engineering College Perundurai Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department of Instrument Technology AUCOE ( A) Andhra University Visakhapatnam Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages.

关键词： Training Analytical models Renewable energy sources Image color analysis Computational modeling Crops Predictive models

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NOMA Cooperative Spectrum Sharing Average Capacity Improvement in 5G Network

NOMA Cooperative Spectrum Sharing Average Capacity Improveme...

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sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mohamed Hassan Rashid A. Saeed Rania A. Mokhtar Khalid Hamid Elmustafa Sayed Ali Nidhal Odeh Manwinder Singh Othman O. Khalifa Shayla Islam Department of Wireless Communication Lovely Professional University Phagwara Punjab India Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Communication Systems Engineering University of Science & Technology Omdurman Sudan Department of Electronics Engineering Sudan University of Science and Technologu (SUST) Khartoum Sudan School of Electrical Computer and Telecommunications Engineering University of Wollongong Australia Department of Electrical and Computer Engineering International Islamic University Malaysia Gombak Malaysia & Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Department of Computer Science Institute of Computer Science and Digital Innovation UCSI University Malaysia Kuala Lumpur Malaysia

One of the most effective approaches to satisfying the requirements of the next generation of wireless services is non-orthogonal multiple access (NOMA). Spectrum efficiency and average capacity are two aspects of wireless communication that have become increasingly important as the number of mobile services in use has increased. Using both traditional MIMO and Massive MIMO (M-MIMO), this study proposes two new methods for increasing the average capacity of a 5G network's downlink (DL) NOMA power domain (PD) in conjunction with a Cooperative Cognitive Radio Network (CCRN). The initial method involves NOMA users competing for available rivalry channels (R-CH) on the CCRN. The second technique creates a dedicated channel (D-CH) for NOMA users. The MATLAB program is utilized to evaluate the proposed methods in three scenarios with varying distances, power location coefficients, and transmission power. Successive interference cancellation (SIC) and unstable channel conditions are also considered while assessing the analysis of the proposed system under the assumption of Rayleigh fading. Using DL NOMA PD systems, The results reveal that the higher average capacity of best at transmit power DL NOMA with CCRN (dedicated channel) improved U4 average capacity performance by M-MIMO DL NOMA with CCRN (D-CH) improved by when compared with convention DL NOMA. Formulas in closed form were found for average capacity. The resulting formulas are verified using a Monte Carlo simulation.

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Unscrambling disease progression at scale: fast inference of event permutations with optimal transport

arXiv

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arXiv 2024年

作者： Wijeratne, Peter A. Alexander, Daniel C. Sussex AI Centre Department of Informatics University of Sussex BrightonBN1 9RH United Kingdom Hawkes Institute Department of Computer Science University College London LondonWC1E 6BT United Kingdom

Disease progression models infer group-level temporal trajectories of change in patients’ features as a chronic degenerative condition plays out. They provide unique insight into disease biology and staging systems with individual-level clinical utility. Discrete models consider disease progression as a latent permutation of events, where each event corresponds to a feature becoming measurably abnormal. However, permutation inference using traditional maximum likelihood approaches becomes prohibitive due to combinatoric explosion, severely limiting model dimensionality and utility. Here we leverage ideas from optimal transport to model disease progression as a latent permutation matrix of events belonging to the Birkhoff polytope, facilitating fast inference via optimisation of the variational lower bound. This enables a factor of 1000 times faster inference than the current state of the art and, correspondingly, supports models with several orders of magnitude more features than the current state of the art can consider. Experiments demonstrate the increase in speed, accuracy and robustness to noise in simulation. Further experiments with real-world imaging data from two separate datasets, one from Alzheimer’s disease patients, the other age-related macular degeneration, showcase, for the first time, pixel-level disease progression events in the brain and eye, respectively. Our method is low compute, interpretable and applicable to any progressive condition and data modality, giving it broad potential clinical utility. © 2024, CC BY.

关键词： Maximum likelihood

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Diabetic Retinopathy Classification Using Hybrid Optimized U-Net and Improved ResNet-18 with MISH Activation

Diabetic Retinopathy Classification Using Hybrid Optimized U...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： S. Nithyapriya S. Yuvalatha S. Prabhavathy T. Arun S.K Muthusundar Antonidoss A Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Electronics and Communication Engineering Christ the King Engineering College Coimbatore India Department of AIDS Chennai institute of technology Chennai TamilNadu India S A Engineering College Chennai India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Diabetic Retinopathy (DR) refers to damage to the retina caused by diabetes, which can cause visual impairments or potentially lead to blindness. The process of manually identifying diabetic retinopathy is slow and can easily be affected by human mistakes because of the eye's complex anatomy. This paper aims to determine the optimal model for accurately staging diabetic retinopathy (DR) across five DR categories. The proposed system involves image pre-processing, segmentation and classification. A well-structured preprocessing framework was implemented, integrating Median Filtering technique to decrease noise and Gamma Correction for improved image quality. Data augmentation is performed through methods like flipping, cropping rotating, and translation of fundus images. In this study, a hybrid optimization strategy is proposed by combining the Adam optimizer with Simulated Annealing (SA) and Cosine Annealing for training a U-Net model for diabetic retinopathy (DR) lesion segmentation. Canny Edge Detection is employed to discover edges correctly within the image. For DR categorisation in this work, enhanced Resnet 18 combined with MISH activation function is used. The suggested model makes use of residual blocks in conjunction with identity and convolutional blocks. Experiments on the APTOS dataset show that the proposed model outperforms state-of-the-art techniques. Accuracy, precision, recall, and F1 score are all improved by the suggested Resnet18 model, which receives scores of 99.45%, 98.64%, 98.63%, and 98.63%, respectively.

关键词： Training Diabetic retinopathy Image segmentation Translation Image edge detection Visual impairment Simulated annealing Retina Sustainable development Optimization

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Novel 6D Pose Estimation for Pick-and-Place Tasks using Model-based Learning

Novel 6D Pose Estimation for Pick-and-Place Tasks using Mode...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Logesh G S. Vinoth Nagarajan Pitchandi A.G. Karthikeyan Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Mechatronics Akshaya College of Engineering and Technology Coimbatore India Department of Mechatronics Bannari Amman Institute of Technology Sathyamangalam India School of Mechanical Engineering REVA University Bangalore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The paper discusses the problem of creating an autonomous SCARA robot that can precisely carry out pick-and-place operations in unstructured settings. The suggested method combines computer vision and deep learning algorithms to provide accurate 6D pose estimation and reliable object detection. Combining YOLOv8 for object detection, hand-eye calibration for precise transformation to robot coordinates, and Principal Component Analysis (PCA) for orientation estimation within the generated point cloud, a novel method for estimating object pose is presented. Monocular depth estimation techniques are used to extract depth information from RGB images to create point clouds. The results demonstrate the potential of combining advanced algorithms with user-friendly design for robotics automation, showing notable gains in pose estimation accuracy and task execution efficiency.

关键词： Point cloud compression Accuracy Service robots Robot kinematics Pose estimation Object detection Manuals Calibration Reliability Principal component analysis

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Edge Caching for Mobile Devices

Edge Caching for Mobile Devices

引用

International Conference on Mathematics, Actuarial science, computer science and Statistics (MACS)

作者： Muhammad Rameez Azeem Syeda Mariam Muzammal Noor Zaman Muhammad Asghar Khan Department of Computer Science Capital University of Science &Technology Islamabad Pakistan University Institute of Information Technology PMAS-Arid Agriculture University Rawalpindi Pakistan School of Computer Science and Engineering Taylor's University Malaysia College of Computer Science and Information Systems Institute of Business Management

ISBN: (纸本)9781665460729

The volume of the network traffic is increasing exponentially. Several smart devices and mobile applications are being widely used, thus placing a heavy pressure on backhaul lines and mobile core networks. For the next-generation portable systems, mobile edge caching is a promising technology with cloud storage capabilities and service environment at the networks edge. Lower latency and faster bandwidth connections are what 5G technology promises. In addition to numerous distant top-level servers that store content, mobile edge caching is able to store popular content or content fragments on certain edge servers close to the end clients. In this paper, we provide a thorough review of mobile edge caching and caching advantages. We also examine several caching standards and how they affect the effectiveness of caching, such as caching prevalent content at edge hubs like base stations which may be a basic arrangement for improving consumers' quality of services. Choosing which content ought to be kept within the base station cache and accurately predicting which content will be popular in the future are both highly difficult tasks. In order to better explain the necessity of improving quality of experience (QoE) in the existing network architecture, we briefly discuss existing work on edge caching and compare quality of service (QoS) and QoE. We describe the caching process in detail along with the open issues and research challenges which will facilitate the research community specifically in the area of edge caching.

关键词： Base stations Quality of service Telecommunication traffic Predictive models Quality of experience Servers Task analysis

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