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13th International Conference on Information Technology in Asia, CITA 2023

作者： Abdulsalam, Nana Hauwa Muhammad, Sufri Mohamed, Raihani Universiti Putra Malaysia Department of Software Engineering and Information System Selangor Serdang43400 Malaysia Universiti Putra Malaysia Department of Computer Science Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350326208

Technology has increased the interest and demand for pervasive systems which require contextual information to function at optimal capacity. There have been numbers of research in context-aware systems that has limited focused on the semantic-based approach in the crowdsourcing domain. Thus, it promotes challenges in the context and service acquisition and representation for reasoning control mechanism. This paper aims to review semantic-based reasoning framework with a focus on the mobile crowdsourcing domain. Different domains acquire different contextual information, either extrinsic or intrinsic. The review of the frameworks has help to formulate a process framework that applied semantic approach that has the important component for context-aware reasoning process. The framework can be used in the context-aware mobile crowdsourcing domain or can be generalized to other domain to aid reasoning control. Its advantage over other crowdsourcing frameworks which is focuses not only on context and service acquisition but also the representation on the acquired information. © 2023 IEEE.

关键词： Semantics

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Joint enhancement of automatic chest X-ray diagnosis and radiological gaze prediction with multi-stage cooperative learning

arXiv

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arXiv 2024年

作者： Qiu, Zirui Rivaz, Hassan Xiao, Yiming Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Concordia University Montreal Canada

Background: As visual inspection is an inherent process during radiological screening, the associated eye gaze data can provide valuable insights into relevant clinical decision processes and facilitate computer-assisted diagnosis. However, the relevant techniques are still under-explored. Purpose: With deep learning becoming the state-of-the-art for computer-assisted diagnosis, integrating human behavior, such as eye gaze data, into these systems is instrumental to help guide machine predictions with clinical diagnostic criteria, thus enhancing the quality of automatic radiological diagnosis. In addition, the ability to predict a radiologist’s gaze saliency from a clinical scan along with the automatic diagnostic result could be instrumental for the end users. Methods: We propose a novel deep learning framework for joint disease diagnosis and prediction of corresponding radiological gaze saliency maps for chest X-ray scans. Specifically, we introduce a new dual-encoder multi-task UNet, which leverages both a DenseNet201 backbone and a Residual and Squeeze-and-Excitation block-based encoder to extract diverse features for visual saliency map prediction, and a multi-scale feature-fusion classifier to perform disease classification. To tackle the issue of asynchronous training schedules of individual tasks in multi-task learning, we propose a multi-stage cooperative learning strategy, with contrastive learning for feature encoder pretraining to boost performance. Results: Our proposed method is shown to significantly outperform existing techniques for chest radiography diagnosis (AUC = 0.93) and the quality of visual saliency map prediction (correlation coefficient = 0.58). Conclusion: Benefiting from the proposed multi-task multi-stage cooperative learning, our technique demonstrates the benefit of integrating clinicians’ eye gaze into radiological AI systems to boost performance and potentially explainability. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning

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Creating an Adaptive Robot for Personalized Health Monitoring and Assistances

Creating an Adaptive Robot for Personalized Health Monitorin...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Ponnuru, Sudhakiran Sai Pranathi, T. Nayak, Gnanesh H Pandi, Maruthu Rishitha Ambati, Sai Kode, Siddeshwar S New Horizon College of Engineering Department of Electronics & Communication Engineering Bangalore India New Horizon College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798331529833

Medical robots are a groundbreaking advancement in healthcare, utilizing robotics and automation to improve precision, efficiency, and accessibility in medical procedures. They are utilized in various applications, including minimally invasive surgeries, rehabilitation, diagnostics, and patient care. Surgical robots, such as the da Vinci Surgical System, are notable examples because they allow physicians to do intricate procedures with greater dexterity and a lower chance of human error. Rehabilitation robots support patient mobility recovery by customizing therapy to meet individual needs and progress. Diagnostic robots utilize imaging technologies to assist in early disease detection and accurate diagnostics, often incorporating artificial intelligence (AI) for image analysis and pattern recognition. The emergence of autonomous and semi-autonomous robots in medical environments promises to alleviate the workload of healthcare professionals, streamline processes, and enhance response times in critical care scenarios. However, the integration of robotics into healthcare also poses challenges, such as ethical dilemmas, regulatory obstacles, and concerns regarding data privacy and patient safety. The future of medical robotics will rely on overcoming these issues while promoting innovation. © 2025 IEEE.

关键词： Medical robotics

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Ether Vote: Revolutionizing Elections with Blockchain-Powered Electronic Voting System 3

Ether Vote: Revolutionizing Elections with Blockchain-Powere...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Joseph, Sijo Pandey, Priyank Khari, Manju Kumar, Kapil Singh, Piyush Pratap School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350319125

Voting is a crucial part of democracy because it gives people the chance to voice their opinions, hold elected officials responsible, encourage diversity in the government, cultivate civic participation, and defend themselves from tyranny. Existing electronic voting (e-voting) systems do in fact suffer a number of important obstacles, with security difficulties and a lack of transparency ranking as two of the most important problems. Given the significance of elections in democracies and the likelihood of fraud or other forms of manipulation in electronic voting procedures, these issues are extremely pressing. systems used for electronic voting heavily rely on software, which might occasionally have vulnerabilities that hackers can exploit. Any weak point in the system can be used to sway elections or jeopardize its security. To address these issues, a strong, secure electronic voting system (EVS), an open system design, impartial audits, and a dedication to inclusion and accessibility are required. For democratic processes to continue to be trusted and the right to vote to be protected, e-voting system integrity must be ensured. It is necessary to create a new Electronic Voting System (EVS) that can offer greater security, speed, and accuracy than the EVS used in the past. The authors of this research suggested a blockchain-based secure EVS. Immutable, transparent, and secure distributed ledger technology named as blockchain. Blockchain used to implement an E-Voting system that is transparent, tamper-proof, and can guarantee the correctness and integrity of the voting process. © 2023 IEEE.

关键词： Blockchain Ethereum EVS Voting

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Deepfake Detection Using EfficientNet and XceptionNet 11

Deepfake Detection Using EfficientNet and XceptionNet

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Yasser, Basma Hani, Jumana El-Gayar, Salma Amgad, Omar Ahmed, Nourhan Ebied, Hala M. Amr, Habiba Salah, Mohamed Ain Shams University Software Engineering Faculty of Computer and Information Sciences Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves preprocessing the Celeb- DF dataset by extracting frames and isolating faces, training the models, and evaluating their performance using log loss and Area Under the Curve (AUC) metrics. The study shows that both models are effective in accurately classifying real and fake videos and highlights the importance of continuously updating deepfake detection algorithms in response to evolving deepfake generation techniques. © 2023 IEEE.

关键词： Fake detection

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A Center-Masked Convolutional Transformer for Hyperspectral Image Classification 2

A Center-Masked Convolutional Transformer for Hyperspectral ...

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2nd Workshop on Complex Data Challenges in Earth Observation, CDCEO 2022

作者： Wang, Yifan Jiang, Shuguo Xu, Meng Zhang, Shuyu Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

Hyperspectral images (HSIs) have a wide field of view and rich spectral information, where each pixel represents a small area of the earth's surface. The pixel-level classification task of HSI has become one of the research hotspots in hyperspectral image processing and analysis. More and more deep learning methods have been proposed in recent years, among which convolutional neural network (CNN) is the most influential. However, it is difficult for CNN-based models to obtain the global receptive field in HSI classification task. Besides, most of the self-supervised training methods are based on sample reconstruction, and it is not easy to achieve effective use of unlabeled samples. In this paper, we propose a novel convolutional embedding module, combined with the Transformer blocks, which successfully improves the context-awareness while retaining the local feature extraction capability. Moreover, a new self-supervised task is designed to make more efficient use of unlabeled data. Our proposed pre-training task only masks the central token and reconstructs the central pixel from a learnable vector. It allows the model to capture the patterns between the central object and surrounding objects without labels. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Pixels

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A Comprehensive Research on Deep Learning Based Routing Optimization Algorithms in software Defined Networks

A Comprehensive Research on Deep Learning Based Routing Opti...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Gaurav Girisha, G.S. Shamanth, N. Dayananda Sagar University School of Engineering Department of Computer Science and Engineering Bengaluru India Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Discovering an optimal routing in software Defined Networks (SDNs) is challenging due to several factors like scalability issues, interoperability, reliability, poor configuration of controllers and security measures. The compromised SDN controller attacks at the control plane layer, packet losses in the topology and end-to-end delay are the most security risk factors in SDNs. To overcome this, in most of the existing researches, Deep Reinforcement Learning (DRL) algorithm with various optimization techniques was implemented for optimal routing in SDN by providing link weights to balance the end-to-end delay and packet losses. DRL used Deterministic Policy Gradient (DPG) method which acts as an actor-critic reinforcement learning agent that searches for an optimal policy to minimize the expected cumulative long-term reward. However, discovering an optimal routing with efficient security measures is still a major challenge in SDNs. This research proposes a detailed review of routing optimization algorithms in SDN using Deep Learning (DL) methods which supports the researchers in accomplishing a better solution for future research. © 2023 IEEE.

关键词： Controller attacks Deep Learning Deterministic Policy Gradient Optimal routing software defined networking

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GUI-Enabled Fusion of U-Net and ResNet-34 for Building Classification and Change Detection 2

GUI-Enabled Fusion of U-Net and ResNet-34 for Building Class...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Vasavi, S. Sai, Yarlagadda Sri, Somagani Hema VR Siddhartha Engineering College Computer Science & Engineering Department Andhra Pradesh India

ISBN: (纸本)9798350383652

Studies predicted that India's urbanization rate have reached 35.9% by 2022. After Tamil Nadu and Kerala, India's third-most urbanized state is Maharashtra. It is home to 45.23% of the nation's urban residents. The primary focus of remote sensing in urban environments has been the classification and detection from satellite images. However, classifying buildings and detecting changes in metropolitan regions using very high resolution satellite (VHRS) images is challenging due to complex architecture and a lack of annotated data. Existing methods for building detection and classification, often lead to low accuracy rates in urban areas due to the variability in building shapes, orientations, and perspectives. To accomplish this task, a Graphical User Interface featuring a deep learning model for semantic segmentation using U-Net with ResNet-34 backbone has been proposed. © 2024 IEEE.

关键词： Satellite imagery

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Overview of image segmentation methods based on deep learning 3

Overview of image segmentation methods based on deep learnin...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Wang, Yiwen Department of Software Engineering and Intelligent Technology Tianjin University of Finance and Economics Tianjin300222 China

ISBN: (纸本)9781510680531

Image segmentation is an important branch of computer vision, which has been widely used in many research fields. In order to understand the research status in the field of image segmentation, this paper systematically analyses and compares the traditional image segmentation method and the image segmentation method based on deep learning. Firstly, the development, advantages, and disadvantages of traditional image segmentation methods are summarized according to the three types of segmentation methods based on threshold, edge, and region. Then, the segmentation network models of three commonly used image segmentation methods based on deep learning, namely, fully convolutional neural network, pyramid scene analysis network, and Mask R-CNN, are analyzed. Based on the previous experimental results and the current research progress based on deep learning models, the problems of contour edge optimization and multi-level pixel information fusion in the field of image segmentation are summarized. At the same time, improvement ideas are put forward for the existing problems. Finally, the improvement and development of future image segmentation methods are prospected, which provides a reference for the subsequent research of segmentation methods. © 2024 SPIE.

关键词： Image segmentation

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An Efficient Autonomous Maze Solving Robot based on Microcontroller by Avoiding Obstacles Using a Decision-Making Algorithm

An Efficient Autonomous Maze Solving Robot based on Microcon...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Zulfiqar, Muhammad Imran Abdullah, Irum Akram, Aasma Mehmood, Shahid Fareeha Ali, Asad Nanjing University of Science & Technology School of Computer Science & Engineering Nanjing China Lahore College for Women University Department of Computer Science Jhang Pakistan Lahore College for Women University Department of Software Engineering Lahore Pakistan Higher Education Department Department of Computer Science Punjab Faisalabad Pakistan Air University Department of Business Administration Multan Pakistan

ISBN: (纸本)9798350315974

The task of maze solving has a rich historical background and continues to hold great significance in the realms of Robotics and Artificial Intelligence. The recent advancements in robotics technology aim to develop self-driving robots capable of autonomous operation, with the goal of streamlining our daily tasks. This emphasizes the focus of most research endeavors on augmenting sensor capabilities and refining algorithms to create robots that are adaptable and *** this paper, we present a contemporary approach to robotics that centers on the creation of self-navigating autonomous robots proficient in maze-solving while avoiding obstacles. This innovative research distinguishes itself from conventional line-following robots that merely adhere to predetermined paths. Instead, our proposed methodology enables the robot to navigate the maze independently, optimizing its path effectively. To achieve this, the robot employed in our study incorporates an ultrasonic sensor and operates on an open-source platform featuring an ATmega328P-based microcontroller *** core of this research lies in the development of a decision-making algorithm, which can be stored within the robot's programmable microcontroller, enabling it to tackle maze-solving tasks. The implications of this research span various applications, such as material handling, warehouse management, pipeline inspection, and bomb disposal. Compared to conventional line-following robots, our method provides superior results in terms of precision, recall, F1-score, time required to solve mazes, and practicality. © 2023 IEEE.

关键词： Microcontrollers

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