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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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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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Trade-Offs of Improper E-waste Recycling: An Empirical Study 1st

Trade-Offs of Improper E-waste Recycling: An Empirical Study

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Talukdar, Md Shamsur Rahman Nurtaj, Marwa Khanom Hasan, Md Nahid Siddeka, Aysha Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chattogram University of Engineering and Technology Chattogram Bangladesh

ISBN: (纸本)9783031346217

The term "E-waste" refers to any electronic equipment that has been abandoned and discarded or reached the end of its service life. As electronics is one of the largest growing industrial sectors, the recycling of E-wastes has evolved into a serious global issue. E-waste contains both metals and chemicals. A growing country like Bangladesh is now estimated to create 2.8 million tons of E-waste every year. Around 15–25% of it gets recycled, while the remaining end up in open soil, farmland, and water. Many hazardous elements from improper E-waste disposal can cause health diseases for workers and their families like breathing problems, skin infections, and stomach problems. Also, improper disposal of E-waste can harm the environment and have a negative impact on our climate. In this paper, we analyze the environmental effects and health issues of electronic waste. We found that there is a significant lack of understanding on how to reuse, rescale, and refurbish electronic equipment. People everywhere may avoid health and environmental problems by managing E-waste effectively, collecting E-waste properly, and raising awareness about the dangers of improper e-waste disposal can save people all over the world from challenges. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Electronic Waste

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Design,simulation,and testing of a tunable MEMS multi-threshold inertial switch

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Microsystems & Nanoengineering 2024年第2期10卷 37-46页

作者： Qiu Xu Rodrigo T.Rocha Yousef Algoos Eric Feron Mohammad I.Younis Faculty of Electronic Information Engineering Huaiyin Institute of TechnologyHuai’an223003PR China Computer Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal23955-6900Saudi Arabia Piezoelectric Microsystem Technologies Silicon Austria LabsVillach9524Austria Physical Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal23955Saudi Arabia Mechanical Engineering Department State University of New YorkBinghamtonNY13902USA

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold *** demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the *** designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing *** adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam *** are shown for the relationships among the applied bias voltage,the duration of shock impact,and the tunable *** fabricated prototypes are tested using a shock-table *** analytical results agree with the experimental *** designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

关键词： tunable acceleration testing

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computer Generated Imagery Animation and Visual Effects Education in India during the Covid-19 pandemic: A significant assessment 2

Computer Generated Imagery Animation and Visual Effects Educ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raturi, Vinod Yadav, Rakesh Kumar Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Maharishi Maharishi University of Information Technology Lucknow India

ISBN: (纸本)9798350305418

The COVID-19 outbreak has been designated an epidemic in India. Lockdown was implemented on March 25, 2020 to fight COVID-19, affecting the country's school system. It has transformed conventional education into an Ed Tech paradigm where teaching and evaluation are done online. This research tries to highlight the challenges Animation and Visual Effects (VFX) educators experience while teaching and assessing online in India. This study used interpretive phenomenological analysis (IPA). Educators from public and private universities were surveyed. Animation and VFX Online instructors and evaluators were semi-structured. The research identified different types of difficulties experience while teaching and assessing online. Lack of basic amenities, external distractions, family disruptions during instruction and evaluation were key difficulties mentioned. Institutional support hurdles included training, technical assistance, and clarity, lack of understanding of online teaching platforms, In addition to a lack of technical understanding was came out from the research. That's good news for institutions that plan to frequently utilize online computer-Generated Imagery (CGI) Animation and Visual Effects (VFX) education and evaluation in the future. © 2023 IEEE.

关键词： animation computer generated imagery (CGI) COVID-19 educator online education outbreak visual effects (VFX)

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Machine Learning based Modeling of Drugs using Virtual Screening and in Silico Approach 5

Machine Learning based Modeling of Drugs using Virtual Scree...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Balamurugan, K.S. Appathurai, K. Sathish Kumar, P.J. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Kg College of Arts and Science Department of Computer Technology Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process and implementation of drug development is a complex structure which is a time-consuming process with higher accuracy. A transformative approach to optimize drug discovery is achieved through the integration of drug discovery with a virtual screening process. This is implemented through the in silico machine learning process. The traditional methods involve extracting and testing numerous chemical compounds in vitro and in vivo in the identification of potential drug contenders. These methods lead to various drawbacks that include higher costs with timelines. To overcome the drawbacks of the existing system, the integration of paradigm modeling is initiated. The behavior of molecules with biological targets is determined through In-silico machine learning algorithms. The effective analysis of millions of chemical compounds is done using a virtual screening process through computational simulations and desired pharmacological effects. The machine learning algorithms help in the extraction of intricate patterns from the molecular dataset. Certain properties such as molecule binding affinity, bioavailability, and toxicity are predicted using these algorithms. They help to optimize the molecular structure in silico to improve the interaction with the target proteins. The reliability is achieved through the quality of the training data with the robustness of the algorithm. They help to generalize newer chemical combinations that provide the solution for various diseases with personalized recommendations through the aid of artificial intelligence. © 2024 IEEE.

关键词： Complex structure Computational simulations Drug development Machine learning process Pharmacological effects Virtual screening process

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Robust Multi-Dialect End-to-End ASR Model Jointly with Beam Search Threshold Pruning and LLM

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SN computer science 2025年第4期6卷 1-12页

作者： Shunmuga Priya, M.C. Karthika Renuka, D. Ashok Kumar, L. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Department of Information Technology PSG College of Technology Coimbatore India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai India

This paper aims to develop a novel robust multi-dialect end-to-end ASR system with beam search threshold pruning. The efficacy of our proposed model is evaluated using word error rate (WER). Our key contributions are: (1) To develop an end-to-end ASR system using attention-based neural network architecture and analyze the effectiveness of two features such as MFCC and log mel filter bank energies on multiple speech dialect corpora including American, Britain, and Indian accents;(2) To integrate beam search threshold pruning as a decoding mechanism to reduce the decoding time (3) To conduct an experimental analysis to test the model performance and compare the results against baseline system. (4) Post processing analysis are carried out using Llama2-7B based large language model(LLM) for enhancing the performance of proposed ASR system. The proposed model significantly improves performance by 1.91% and 4.29% over clean and noisy speech in librispeech corpus. Similarly, for the Indian accented speech, the model attains an average WER of about 6.6%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Automatic speech recognition Beam search Decoding LLM Log Mel filter bank energies

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Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources 10

Harmonizing Healthcare Perspectives and In-Depth Strategy fo...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Manikandan, A. Thirumal, S. Kumar, N. Vels Institute of Science Technology &advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration. © 2024 IEEE.

关键词： Electronic health record

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Metrics Evaluation of Bell Pepper Disease Classification Using Deep Convolutional Neural Network (DCNN) 1

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2nd International Conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Sowmiya, K. Thenmozhi, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

Crop disease is one of the major issues in agricultural fields. It reduces the food production process and produces a huge economic loss for farmers in agricultural lands. Deep learning models are built for detecting and identifying diseases at an early stage to help the farmers and provide food security. In this paper, transfer learning approach is used to identify and classify the diseased portion of leaves in farming lands. The benchmark dataset was taken from the Internet (Kaggle) for bell pepper leaf for identifying the diseases such as pepper leaf spot, mosaic virus, blight, damping off disease, verticillium wilt, blossom end rot, yellowing leaves, curling leaves, and healthy leaves. Ten pre-trained models are built for recognizing diseases and compared to obtain better accuracy. Adam optimizer and binary cross-entropy is used in the model for calculating loss function. In this paper, experimental results are presented on bell pepper diseases for MobileNetV2 with better accuracy of 99.42%. The various performance metrics such as accuracy, precision, recall and F1-score, ROC curve are used to determine the accuracy of the model. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Crops

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An Explainable Deep Learning Framework for Multi-Class Skin Lesion Classification While Resolving Class Imbalance 2

An Explainable Deep Learning Framework for Multi-Class Skin ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Jahan, Israt Efat, Anwar Hossain Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Srizon, Azmain Yakin Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are indicative of various skin diseases, including serious conditions like melanoma, which can be life-threatening if not detected early. Automated systems for analyzing dermatoscopic images have shown promise in improving diagnostic accuracy. However, many existing studies rely on pre-trained convolutional neural network (CNN) architectures without customization, leading to potential biases and suboptimal performance. In this study, we propose a customized DenseNet121 architecture tailored for skin lesion classification. We address class imbalance issues through data augmentation and fine-tune the model to extract fine-grained features. Our method surpasses other cutting-edge models in terms of accuracy, precision, recall, specificity, sensitivity, and root mean square (ROC) values. Furthermore, we utilise Gradient-weighted Class Activation Mapping (Grad-CAM) to visually represent the decision-making process of the model. The next study will prioritise the validation of the model using a wide range of datasets and the integration of lesion-related information to enhance its performance. © 2024 IEEE.

关键词： Convolutional neural networks

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