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Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm

Journal of Supercomputing 2023年第7期79卷 7067-7093页

作者： Feng, Junhong Zhang, Jie Zhu, Xiaoshu Wang, Jian-Hong School of Computer and Information Engineering Guangdong Songshan Polytechnic Guangdong Shaoguan512126 China School of Computer Science and Engineering Yulin Normal University Guangxi Yulin537000 China Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Genetic algorithms

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From Past to Present: A Comprehensive Review of Antenna Technology in Modern Wireless Communication

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Journal of engineering science and Technology Review 2024年第3期17卷 179-200页

作者： Mishra, Brijesh Singh, Aditya Kumar Satheesha, T.Y. Verma, Ramesh Kumar Singh, Vivek Department of Electronics and Communication Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology U.P. Greater Noida India School of Computer Science Engineering REVA University Karnataka Bengaluru India Department of Computer Science and Engineering IMS Engineering College Uttar Pradesh Ghaziabad India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Karnataka Bengaluru India

This research looks at microwave devices, specifically, patch antenna along with electromagnetic spectrum, shape, mechanism, analytical methods, simulation tools, and feeding procedures. Patch antennas are distinguished by their rectangular form, with a patch on one side and a ground plane on the other. Patch antennas work by exciting electromagnetic waves inside the patch, which are subsequently transmitted into the surrounding environment. The report also outlines numerous ways for evaluating the performance of microstrip patch antennas. The electromagnetic characteristics of the antenna are analyzed using the transmission line model, cavity model, and multiport network model (MNM). The integral equations that regulate the behavior of the antenna are solved using the method of moments (MoM) and the finite element method (FEM). The spectral domain technique (SDT) is used to analyze the antenna’s frequency response, while the finite difference time domain (FDTD) approach is used to analyze the antenna’s time-domain behavior. Overall, these methodologies give a thorough understanding of microstrip patch antenna performance and may be utilized to optimize their design. Furthermore, several patch antenna feeding methods, such as probe feed, microstrip line feeding, aperture coupling, proximity coupling, and CPW feed, are investigated. Attaching a microstrip line to the patch, which is subsequently linked to the RF source, is what microstrip line feeding entails. Aperture coupling entails making a hole in the ground plane that allows the RF source to feed the patch directly. Proximity coupling is accomplished by placing a probe near the patch, which creates an electromagnetic field on the patch. Patch antenna simulation software includes programmers such as HFSS, CST, and FEKO. These tools simulate the patch antenna’s performance, including its radiation pattern, gain, and input impedance. These simulations may be used to optimize the patch antenna design for specific a

关键词： Microstrip antennas

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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A Robust Convolutional Neural Network Architecture for Automated Pneumonia Detection from Chest X-ray Images 3

A Robust Convolutional Neural Network Architecture for Autom...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Babu, Tina Naik, Pramod Kumar Nair, Rekha R Pallavi, K. Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350354171

Pneumonia is a significant global health concern, especially for vulnerable populations such as children under five. Timely and accurate diagnosis is critical for effective treatment and improved patient outcomes. This project proposes a robust solution leveraging Convolutional Neural Networks (CNNs) for automated pneumonia detection from chest X-ray images. The model, trained on a meticulously preprocessed dataset of 5,216 chest X-ray images, demonstrated exceptional performance, achieving a training accuracy of 98.16% and a validation accuracy of 98.45% after 50 epochs. The architecture comprises five Conv2D layers with ReLU activation, followed by max-pooling layers, and a fully connected artificial neural network for classification. The model's success in distinguishing between normal and pneumonia-infected cases underscores its potential as a valuable tool for healthcare professionals, offering a reliable and efficient means of pneumonia identification. The model's high accuracy and efficiency make it a valuable asset, potentially reducing the workload on radiologists and enabling quicker diagnoses, ultimately leading to more timely interventions and improved patient outcomes. Comprehensive evaluation, including testing on a separate dataset and real-world image inferences, validates the robustness of the proposed solution. © 2024 IEEE.

关键词： Convolutional neural networks

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TotalCT-SAM: A Whole-Body CT Segment Anything Model with Memorizing Transformer 27th

TotalCT-SAM: A Whole-Body CT Segment Anything Model with Mem...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Zhiwei Shen, Yiqing School of Computer Engineering and Science Shanghai University Shanghai China Department of Computer Science Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783031781971

Whole-body computed tomography (CT) is a crucial medical imaging modality that provides a comprehensive view of tissue anatomy, facilitating the detection and diagnosis of various conditions, including cancer, trauma-related injuries, vascular abnormalities, infectious diseases, and organ pathologies. However, existing deep learning methods for whole-body CT segmentation, such as nnU-Net, often suffer from limited generalization capabilities, hindering their adaptability to diverse clinical needs and real-world scenarios. To address this issue, we propose a novel interactive semantic segmentation method based on the recently introduced Segment Anything Model (SAM), which employs a foundation model approach to enable flexible instance segmentation. Our method extends SAM to overcome its limitation of losing important semantic information during the segmentation process, making it specifically tailored for whole-body CT anatomy segmentation. The proposed approach allows for both prompt-free and prompt-guided segmentation, accommodating different use cases and providing enhanced flexibility. Furthermore, we introduce a memory bank module that expands the context of the self-attention mechanism through approximate k-nearest neighbor (KNN) lookup, enabling the model to capture long-range dependencies and attend to distant relevant features, thereby improving its ability to handle the complexities of whole-body CT data. Experimental results demonstrate that our method achieves competitive performance compared to other state-of-the-art approaches while preserving rich semantic information at the pixel level. Our code is publicly available at https://***/13482108753/Totalct-SAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Hybrid blockchain based medical data sharing with the optimized CP-ABE for e-Health systems

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International Journal of Information Technology (Singapore) 2024年第1期16卷 121-130页

作者： Mishra, Anil Kumar Mohapatra, Yogomaya School of Computer Science and Engineering Trident Academy of Technology Odisha Bhubaneswar India Department of Computer Science and Engineering C.V. Raman Global University Odisha Bhubaneswar India

While e-Health systems must prioritise security and privacy, sharing medical data can help improve diagnostic accuracy. Because of its immutability, Blockchain (BC) is now being suggested as an exciting solution for sharing Personal Health Information (PHI). However, sharing PHI must improve patient privacy and security. As a result, this paper describes a secure medical data sharing system. Private BC, Consortium BC (CBC), and optimized cryptography are all included in the proposed system. To ensure patient privacy, PHI is encrypted using optimised ciphertext policy attribute based encryption (CP-ABE). In particular, replicated attributes amendment (RAA) is proposed to improve CP-ABE performance. For each replicated attribute, this RAA achieves a single share value. PHI ciphertexts are stored as blocks in the hospital’s private BC. Furthermore, the PHI keyword is saved to the CBC. By validating the keyword and getting access to the private BC, the doctor may retrieve all patient’s PHI. The article’s findings show that the proposed system achieves lower storage overhead, encryption time, and decryption time. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain (BC) CBC e-Health system Replicated attributes amendment

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Belief Combination of Classifiers for Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 652-667页

作者： Zuowei Zhang Songtao Ye Yiru Zhang Weiping Ding Hao Wang IEEE the Research Center for Optical Fiber Sensing Zhejiang Laboratory the School of Automation Northwestern Polytechnical University the School of Cyber Engineering Xidian University the Department of Computer Science CY Cergy Paris Université the School of Information Science and Technology Nantong University the School with of Cyber Engineering Xidian University the Department of Computer Science Norwegian University of Science and Technology

Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of *** this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence *** proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by *** BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a ***,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query ***,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query *** weights consist of two aspects:global and *** global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern *** comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs.

关键词： Classifier fusion classification evidence theory incomplete data missing values

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Kathakali Mudras: An AI-Enhanced Predictive Approach 11

Kathakali Mudras: An AI-Enhanced Predictive Approach

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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BAHGRF^(3):Human gait recognition in the indoor environment using deep learning features fusion assisted framework and posterior probability moth flame optimisation

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 387-401页

作者： Muhammad Abrar Ahmad Khan Muhammad Attique Khan Ateeq Ur Rehman Ahmed Ibrahim Alzahrani Nasser Alalwan Deepak Gupta Saima Ahmed Rahin Yudong Zhang Foundation University Islamabad IslamabadPakistan Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl KhobarSaudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadhSaudi Arabia Department of Computer Science Maharaja Agrasen Institute of TechnologyDelhiIndia United International University DhakaBangladesh School of Informatics University of LeicesterLeicesterUK

Biometric characteristics are playing a vital role in security for the last few *** gait classification in video sequences is an important biometrics attribute and is used for security purposes.A new framework for human gait classification in video sequences using deep learning(DL)fusion assisted and posterior probability-based moth flames optimization(MFO)is *** the first step,the video frames are resized and finetuned by two pre-trained lightweight DL models,EfficientNetB0 and *** models are selected based on the top-5 accuracy and less number of ***,both models are trained through deep transfer learning and extracted deep features fused using a voting *** the last step,the authors develop a posterior probabilitybased MFO feature selection algorithm to select the best *** selected features are classified using several supervised learning *** CASIA-B publicly available dataset has been employed for the experimental *** this dataset,the authors selected six angles such as 0°,18°,90°,108°,162°,and 180°and obtained an average accuracy of 96.9%,95.7%,86.8%,90.0%,95.1%,and 99.7%.Results demonstrate comparable improvement in accuracy and significantly minimize the computational time with recent state-of-the-art techniques.

关键词： deep learning feature fusion feature optimization gait classification indoor environment machine learning

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Exhibiting the Explicit Aspects in Twitter Sentimental Data

Exhibiting the Explicit Aspects in Twitter Sentimental Data

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Jayanthi, S. Arumugam, S.S. Saveetha School of Engineering Department of Computer Science & Engineering India

ISBN: (纸本)9798350348910

The evolution of social networks has resulted in medium to express feelings and thoughts of an individual to global audience, thanks to the rapid expansion of the Internet. Many individual utilize textual content, images, audio, and videos to convey their emotions and perspectives. However, the sheer volume of text-based communication on social media can be overwhelming. Each second, an enormous quantity of unstructured information is created online through these platforms. To comprehend human psychology in real-time, this data needs to be processed promptly, and one way to achieve this is through sentiment analysis. Sentiment analysis identifies the polarity of texts, determining whether the author holds a opinion of a negative, favourable, or neutral nature product, service, person, or location. In certain applications, sentiment analysis alone may be insufficient, necessitating the inclusion of emotion detection to accurately determine condition of one's mind or heart. Sentiment analysis and emotion detection from textual data are both covered in depth in this work, with explicitly revealed insights into the distinct stages of sentiment analysis and the corresponding emotion models. © 2023 IEEE.

关键词： Sentiment analysis

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