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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Nagarathna, S.B. Gehlot, Anita Tiwari, Mohit Tiwari, Tripti Chakravarthi, M. Kalyan Verma, Devvret Ctae Mpuat Department of Processing and Food Engineering Rajasthan Udaipur India Uttaranchal Institute of Technology Uttaranchal University India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Bvimr Department of Management Studies Delhi India School of Electronics Engineering VIT-AP University India Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The recent pandemic situation has restricted the mass movements of researchers around the world and thus, scientists and researchers are unable to go to respective places for real-time monitoring of some complex experiments. This ultimately resulted in the failure of experiments due to a lack of real-time monitoring by scientists. To overcome this situation, a low-cost and efficient real-time monitoring technology is necessary to be developed. Thus, the research focused on a cloud-based IoT technology that aids the real-time monitoring of cell cultures in laboratories. Findings suggested that people agreed with the use of 'Message Queuing Telemetry Transport' (MQTT) devices for sending short messages to the Cell-culture device. Redis device is required for real-time monitoring at every 30 seconds which is not required in cell-culture monitoring. MQTT is the most important device to send short messages to the cell culture machine. Thereafter, the machine functions using the messages and uploads output on Ceph/S3. The outputs are visualised through a web-based Unique Identifier (UI) portal. © 2022 IEEE.

关键词： Cloud computing

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Causal-IQA: towards the generalization of image quality assessment based on causal inference 24

Causal-IQA: towards the generalization of image quality asse...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yan Zhong Xingyu Wu Li Zhang Chenxi Yang Tingting Jiang School of Mathematical Sciences and National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR China Hefei Institute of Physical Science Chinese Academy of Sciences University of Science and Technology of China Hefei China National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science and 5National Biomedical Imaging Center Peking University Beijing China

Due to the high cost of image Quality Assessment (IQA) datasets, achieving robust generalization remains challenging for prevalent deep learning-based IQA methods. To address this, this paper proposes a novel end-to-end blind IQA method: Causal-IQA. Specifically, we first analyze the causal mechanisms in IQA tasks and construct a causal graph to understand the interplay and confounding effects between distortion types, image contents, and subjective human ratings. Then, through shifting the focus from correlations to causality, Causal-IQA aims to improve the estimation accuracy of image quality scores by mitigating the confounding effects using a causality-based optimization strategy. This optimization strategy is implemented on the sample subsets constructed by a Counterfactual Division process based on the Backdoor Criterion. Extensive experiments illustrate the superiority of Causal-IQA.

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

An evolutionary lion optimization algorithm-based image comp...

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作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Gender Detection using Facial Features with Support Vector Machine

Gender Detection using Facial Features with Support Vector M...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Manjunath S. Chincholi Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum Karnataka India Specialist in Image Processing Tata Elxsi Limited Bangalore Karnataka India Principal Engineer (Artificial Intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore Karnataka India Dept. of Image Processing eMath Technology Pvt. Ltd. Bangalore Karnataka India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665491129

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In image processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.

关键词： Support vector machines Wireless communication Training Machine learning algorithms Face recognition Support vector machine classification Feature extraction

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Insights into performance evaluation of com-pound-protein interaction prediction methods

arXiv

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arXiv 2022年

作者： Yaseen, Adiba Amin, Imran Akhter, Naeem Ben-Hur, Asa Minhas, Fayyaz Islamabad Pakistan National Institute for Biotechnology and Genetic Engineering Faisalabad Pakistan Department of Computer Science Colorado State University Fort Collins United States Tissue Image Analytics Centre Department of Computer Science University of Warwick Coventry United Kingdom

Motivation: Machine learning based prediction of compound-protein interactions (CPIs) is important for drug design, screening and repurposing studies and can improve the efficiency and cost-effectiveness of wet lab assays. Despite the publication of many research papers reporting CPI predictors in the recent years, we have observed a number of fundamental issues in experiment design that lead to over optimistic estimates of model performance. Results: In this paper, we analyze the impact of several important factors affecting generalization performance of CPI predictors that are overlooked in existing work: 1. Similarity between training and test examples in cross-validation 2. The strategy for generating negative examples, in the absence of experimentally verified negative examples. 3. Choice of evaluation protocols and performance metrics and their alignment with real-world use of CPI predictors in screening large compound libraries. Using both an existing state-of-the-art method (CPI-NN) and a proposed kernel based approach, we have found that assessment of predictive performance of CPI predictors requires careful control over similarity between training and test examples. We also show that random pairing for generating synthetic negative examples for training and performance evaluation results in models with better generalization performance in comparison to more sophisticated strategies used in existing studies. Furthermore, we have found that our kernel based approach, despite its simple design, exceeds the prediction performance of CPI-NN. We have used the proposed model for compound screening of several proteins including SARS-CoV-2 Spike and Human ACE2 proteins and found strong evidence in support of its top hits. © 2022, CC BY-SA.

关键词： Proteins

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TSdetector: Temporal-Spatial Self-correction Collaborative Learning for Colonoscopy Video Detection

arXiv

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arXiv 2024年

作者： Wang, Kai-Ni Wang, Haolin Zhou, Guang-Quan Wang, Yangang Yang, Ling Chen, Yang Li, Shuo School of Biological Science and Medical Engineering Southeast University Nanjing China Jiangsu Key Laboratory of Biomaterials and Devices Southeast University Nanjing China Laboratory of Image Science and Technology Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China TUGE Healthcare Nanjing China Institute of Medical Technology Peking University Health Science Center China Department of Computer and Data Science Department of Biomedical Engineering Case Western Reserve University United States

CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://***/soleilssss/TSdetector. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Application of binary classification of magnetic resonance therapy images to detect the threat of the disease due to COVID-19 3

Application of binary classification of magnetic resonance t...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Zalevska, Olga Vanin, Volodymyr Sydogenko, Julia Finogenov, Oleksii Demydenko, Olga Yablonskyi, Petro Ladogubets, Tetyana Oliinyk, Yurii Miroshnichenko, Ivan National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Automation of Power Processes and Systems Engineering Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Descriptive Geometry Engineering and Computer Graphics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Computer-Aided Science and Data Processing Systems Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' The Department of Theory Practice and Translation of English Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Deprtament of Applied Mathematics Kyiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The paper considers the use of a neural network for the binary classification of magnetic resonance imaging images to establish a possible disease of COVID-19. processing of input data and their reduction to one format is provided, and neural network software for binary classification is developed and its training based on about 60,000 open patient images. The using of developed software is universal for medical doctors or ordinary Internet users and does not require additional training. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Exploring Contextualized Embeddings for C++ Code Search and Algorithms Classification

Exploring Contextualized Embeddings for C++ Code Search and ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Robert R. Khazhiev Darya A. Skvortsova Sergey N. Golubev Lev R. Barabanshchikov Industrial Logistic Department Bauman Moscow State Technical University Moscow Russian Federation Dept. of Information Processing and Management Systems Bauman Moscow State Technical University Moscow Russian Federation Institute of Applied Computer Science ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Contextualized embeddings of words (such as BLOOM and BERT) outperform static embeddings (such as Word2vec, Fasttext, and GloVe) for many natural language processing tasks. In this article, an exploration is conducted of the use of contextualized embeddings for the purposes of searching for C++ code and classifying the algorithms that are utilized. Furthermore, a novel approach is proposed for reducing the dimensionality of the resulting embeddings, leveraging a multilayer perceptron trained with Multi Similarity Loss. The experiments performed for the methods of multilabel classification of algorithms and for the code similarity search show that the proposed method not only effectively reduces the dimension of embeddings, but also increases their representativeness.

关键词： Measurement Power engineering Codes Source coding Large language models C++ languages Multi label classification Multilayer perceptrons Natural language processing

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