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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-source Word-aligned Attention Average Pooling based Bidirectional Encoder Representation from Transformers for Product Review Sentiment Analysis 4

Multi-source Word-aligned Attention Average Pooling based Bi...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Prabhu, Rakshitha Nashappa, Chandrashekara Seesandra S.J.C. Institute of Technology Department of Computer Science and Engineering Chikkaballapur India C. Byre Gowda Institute of Technology Department of Computer Science and Engineering Kolar India

ISBN: (纸本)9798350393354

The product review gives critical data for both businesses and consumers, offering insights needed before buying a service or product. However, the existing methods has drawback of there is not understanding semantic relationship among adjacent characters. To overcome these limitations in this research proposed a Multi-source Word-aligned Attention Average Pooling based Bidirectional Encoder Representation from Transformers (MWAAP-BERT) model for product review sentiment analysis. The dataset utilized for the research is Amazon product review dataset and the data is per-processed by several methods. The Term Frequency-Inverse Document Frequency (TF-IDF) and Skip N-gram methods are used for feature extraction and proposed MWAAP-BERT model is used for product review sentiment classification. Performance of proposed method is estimated with performance measure of accuracy, precision, recall and f1-score. Proposed technique attained high accuracy of 97.5 %, precision of 96.5%, recall of 96.5% and f1-score of 96.5% which is superior than other existing methods like Recurrent Neural Network (RNN), Taylor-Harris Hawks Optimization driven Long Short-term Memory (THHO-BiLSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) © 2024 IEEE.

关键词： Sentiment analysis

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Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins 1

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Seelaboyina, Radha Vadla, Sai Prakash Chary Teerthala, Sree Alekhya Pedduri, Veena Vani Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India Department of Information Technology Geethanjali College of Engineering and Technology Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

Digital Twin is an implementation of a physical object or system into a digital space for efficient monitoring and better scalability. It is a virtual representation which acts as a digital counterpart for Cyber Physical Systems or processes in real-time and addresses every instance of its total life cycle. Nevertheless, it also has huge prospects even in the conventional and well-developed industries like agriculture, construction, medicine and automation. By large, it serves as a key example of how the Industry4.0 concept is revolutionizing the industries by facilitating vast numbers of opportunities and efficiencies in them. In this paper, we dig a deep dive into the various risks of cybersecurity associated with Cyber Physical Systems, Digital Twin technology and explore the opportunities of twins protecting themselves. Additionally, we look at the implementation of Secure Software Development Life-Cycle (SSDLC) on Digital Twins and Cyber Physical Systems for mitigating the proposed risks from the very roots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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An Efficient Vehicle Detection and Shadow Removal Using Gaussian Mixture Models with Blob Analysis for Machine Vision Application

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SN computer science 2023年第5期4卷 451页

作者： Rajkumar, S. Hariharan, A. Girish, S. Arulmurugan, Manoj School of Computer Science and Engineering Vellore Institute of Technology Chennai India

Cities in the modern era often face a hectic challenge due to increased urbanization and industrialization. This problem arises as the number of cars on the road increases over time, and the need for traffic data becomes important. This increase is not foreseen with the creation of suitable new road sections. Accordingly, in this paper, we developed a novel algorithm to detect and count vehicles of each frame from a CCTV footage after enhancing it accordingly. Morphological filtering is used for removal of noise from each frame. The vehicles are detected using blob analysis and detected as well as counted by Gaussian mixture models. Finally, we compare our proposed method’s results against a suitable and a recent method’s YOLO (you only look once algorithm) result. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bilateral filtering Blob analysis CCTV Foreground extraction Gaussian mixture models Morphological filtering

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Fast Detection and Traceback-Based Mitigation of Interest Flooding Attack

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SN computer science 2025年第2期6卷 1-15页

作者： Kumar, Naveen Singh, Ashutosh Kumar Srivastava, Shashank Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395007 India Department of Computer Science Allahabad Degree College University of Allahabad Uttar Pradesh Prayagraj 211003 India Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj 211004 India

The Pending Interest Table (PIT) in Named Data Networking (NDN) plays a crucial role by storing state information of requests within the router, enabling efficient data packet routing back to the requester. However, this mechanism is vulnerable to Interest Flooding Attacks (IFA), where an attacker sends a large number of malicious requests to overwhelm the PIT, disrupting network performance. Previous research primarily focused on offline detection of IFA using selected features and machine learning techniques. In this work, we build on these findings by deploying a trained Artificial Neural Network (ANN) classifier on each NDN router for real-time, online detection of IFA. Additionally, we introduce a novel traceback-based mitigation strategy activated upon detection, significantly improving the network’s resilience against such attacks. Our proposed method demonstrates superior performance in terms of satisfaction ratio and throughput for legitimate consumers compared to existing approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： ANN Feature selection Interest Flooding Attack Named Data Networking NDN Traceback

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Influence of Nodes in Complex Networks 14

Influence of Nodes in Complex Networks

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Hansda, Raimoni Chakraborty, Susanta Indian Institute of Engineering Science and Technology Department of Computer Science and Technology Howrah India

ISBN: (纸本)9798350335095

Network measuring metric is a salient research topic in Complex networks. Each metric works effectively to explore certain characteristics based on the application requirements. Degree centrality and its various extensions are popular metrics for evaluating the nodes' influence in complex networks. The Weighted Degree Centrality (WDC) effectively determines the nodes' influence in weighted and undirected graphs. The WDC fails to distinguish the influence between the same weighted degree nodes. A new network measuring metric Node Contribution Factor (NCF) has been introduced to overcome this problem. The NCF effectively evaluates the nodes' influence by considering the neighbours' connectivity in weighted and undirected networks. This proposed metric has been appraised in varied structures of network. The metric has been examined on benchmark datasets to show the superiority of the suggested strategy. The proposed metric outperforms the state-of-the-art network measuring metrics to calculate the nodes' influence in the weighted and undirected networks. © 2023 IEEE.

关键词： Complex networks

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Application of Generative Graph Models in Biological Network Regeneration: A Selective Review and Qualitative Analysis

Application of Generative Graph Models in Biological Network...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Teji, Binon Roy, Swarup Guzzi, Pietro Hiram Bhandari, Dinabandhu Lab Department of Computer Applications Gangtok India University of Catanzaro School of Computer Science and Biomedical Engineering Italy Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350386226

Biological networks are essential for understanding the complex cellular mechanisms of living organisms. Simulating biological networks allows researchers to model and understand complex cellular processes without the need for extensive and costly experiments. The ability to generate synthetic graphs that closely resemble these real-world, complex biological processes is a vital research area. Graph generation in the context of biological networks is particularly significant because it can lead to insights into cellular functions, disease mechanisms, and therapeutic targets. Recent advances in deep learning, particularly in graph generative models, have opened new avenues for applications in the biological domain. These advancements have the potential to revolutionize our understanding of biological systems. However, despite the development of numerous effective graph generation models, there has been limited work assessing the qualitative aspects of the generated graphs within the context of biological networks. Addressing this gap is crucial for ensuring that synthetic graphs are not only structurally accurate but also biologically *** various graph generation models are available, their application to biological network recreation has been limited. In this paper, we focus on graph generation, specifically edge and node-independent models for biological networks. We assess four candidate models across four gene expression networks. Our systematic assessment examines the models' qualitative aspects, including graph structural properties, generation diversity, and computational efficiency. Our findings highlight the strengths and limitations of current models, offering insights to guide the development of more robust graph generation techniques that accurately replicate biological network characteristics. © 2024 IEEE.

关键词： Biotic

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Real-Time Object Detection from Surveillance using Deep Learning 3

Real-Time Object Detection from Surveillance using Deep Lear...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Tanisha, A. Tanisha, N. Chaitra, M. Tejasri, P.R. Bnm Institute of Technology Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecting weapons in both pre-recorded videos and live camera feeds. The model, trained on a dataset with 4000 images labeled for handguns and knives, utilizes image preprocessing steps such as resizing, normalization, and augmentation to improve detection accuracy. Implementing the system with OpenCV and Tkinter, the application processes video streams, identifying potential threats with high precision rate of 0.85 and recall rate of 0.90. The integration of a graphical user interface ensures user-friendly operation for security personnel. Key outcomes demonstrate the efficacy of deep learning models in real-time object detection, significantly contributing to proactive surveillance and enhanced situational awareness. © 2025 IEEE.

关键词： Graphical user interfaces

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An Adaptive Classifier Based Approach for Crowd Anomaly Detection

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computers, Materials & Continua 2022年第7期72卷 349-364页

作者： Sofia Nishath P.S.Nithya Darisini School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep *** this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded *** use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking *** technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video *** use the multi-objective whale optimization algorithm to optimize the entire process and get the best *** performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the Python simulation *** simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.

关键词： Abnormal event detection adaptive GoogleNet neural network classifier multiple instance learning multi-objective whale optimization algorithm

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Local Neighborhood Average Pattern: A Handcrafted Feature Descriptor for Hand Gesture Recognition 3

Local Neighborhood Average Pattern: A Handcrafted Feature De...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Bahuguna, Arti Namchyo, Shalom Ben Tenzing Chaudhary, Deepak Kumar Bhaumik, Gopa Govil, Mahesh Chandra National Institute of Technology Sikkim Computer Science and Engineering Sikkim India National Institute of Technology Jamshedpur Computer Science and Engineering Jharkhand India

ISBN: (纸本)9798350300710

This paper proposes a handcrafted feature-based descriptor namely Local neighborhood average pattern (LNAP) for static hand gesture recognition. The fact, that the local descriptors are important in numerous computer vision applications and show great performance cannot be overstated. This is accentuated further in difficult environments. This is the major driving force behind continued research in this field. We developed a feature descriptor LNAP in this paper, with the aim of extracting complete microstructural features achieved by evaluating excitation in different ways and directional relevant data based on connections between pixels taken at various spatial configurations inside each 3 × 3 neighborhood. An LNAP descriptor represents the structure of hand textures in a simple and compact coding manner, resulting in its unique code in less time and memory than existing approaches. The proposed LNAP descriptor extracts the dominant features from the hand region which are further classified using SVM. The proposed LNAP's performance is assessed using three benchmark datasets: Massey University Dataset (MUGD), ASL Digit Datasets, and Ouhands Dataset in respect of accuracy and F1-score and found to achieve an accuracy of 93% (MUGD Set1), 96% (MUGD Set2), 92 % (MUGD Set 3), 89% (MUGD Set4), 71% (MUGD Set5), 99%, and 47% respectively. The experimental findings show that the suggested LNAP produces better outcomes than existing techniques. © 2023 IEEE.

关键词： Textures

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