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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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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Efficient Deep Learning Models for Toxic Comments Identification and Classification Using LSTM Network 12th

Efficient Deep Learning Models for Toxic Comments Identifica...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Tiwari, Nandini Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9789819789450

An effective strategy for identifying and categorizing toxic comments is urgently needed due to the overwhelming number of such comments on various internet platforms. While previous research has addressed various forms of toxic behaviour, existing approaches often fall short. Our research fills this need by suggesting a novel method that combines ML with natural language processing (NLP). Building on previous work, we focus on recurrent neural networks (RNNs), specifically LSTM networks, renowned for their adeptness in capturing sequential patterns and contextual information within text. Also, we incorporate dropout layers to enhance model generalization and combat overfitting. Evaluating our models on a benchmark dataset reveals promising outcomes, with the LSTM model achieving an accuracy score of 0.9943 and a loss of 0.0493, while the LSTM model with dropout layers closely follows with an accuracy score of 0.9939 and a loss of 0.0493. These findings underscore how effectively our methodology detects toxic comments, thereby advancing automated moderation systems. Looking ahead, we aim to refine our approach and explore avenues for real-world implementation, aiming to foster healthier online discourse and enhance user safety. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An effective object detection and tracking using automated image annotation with inception based faster R-CNN model

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 343-356页

作者： Vijiyakumar, K. Govindasamy, V. Akila, V. Department of Computing Technologies School of computing SRM institute of Science and Technology Kattankulathur campus Tamilnadu Chengalpattu district India Department of Information Technology Pondicherry Engineering College Pondicherry India Department of Computer Science and Engineering Pondicherry Engineering College Pondicherry India

The present study advances object detection and tracking techniques by proposing a novel model combining Automated Image Annotation with Inception v2-based Faster RCNN (AIA-IFRCNN). The research methodology utilizes the DCF-CSRT model for image annotation, Faster RCNN for object detection, and the inception v2 model for feature extraction, followed by a softmax layer for image classification. The proposed AIA-IFRCNN model is evaluated on three benchmark datasets: Bird (Dataset 1), UCSDped2 (Dataset 2), and Under Water (Dataset 3), to determine prediction accuracy, annotation time, Center Location Error (CLE), and Overlap Rate (OR). The experimental results indicate that the AIA-IFRCNN model outperformed existing models regarding detection accuracy and tracking performance. Notably, it achieved a maximum detection accuracy of 95.62 % on Dataset 1, outperforming other models. Additionally, it achieved minimum average CLE values of 4.16, 5.78, and 3.54, and higher overlap rates of 0.92, 0.90, and 0.94 on the respective datasets (1, 2 and 3). Hence, this research work on object detection and tracking using the AIA-IFRCNN model is essential for improving system efficiency and fostering innovation in the field of computer vision and object tracking. © 2024

关键词： Image annotation

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A comprehensive review of effective parameters to improve the performance of the Savonius turbine using a computational model and comparison with practical results

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Water-Energy Nexus 2024年第1期7卷 266-274页

作者： Jebelli, Ali Lotfi, Nafiseh Saeid Zare, Mohammad Yagoub, Mustapha C.E. Department of Engineering and Design Robotic Century Inc Alberta Canada Faculty of New Science and Technologies Tehran University Tehran Iran School of Electrical Engineering and Computer Science University of Ottawa ON Canada

Amidst growing global concerns over climate change and escalating greenhouse gas emissions from fossil fuels, the pursuit of renewable energy sources has become critical. This study focuses on harnessing hydropower using Savonius turbines, which are known for their efficiency in generating energy at lower flow rates. However, the intrinsic low efficiency of these turbines necessitates precise optimization tailored to specific river or channel conditions. In this research, we optimized the performance of Savonius turbines by analyzing key parameters such as the height-to-diameter ratio, blade twist, and the integration of multi-stage configurations with deflectors. Our findings reveal significant efficiency improvements through strategic modifications. Specifically, by reducing the height-to-diameter ratio from 1.2 to 0.8 and maintaining a Tip Speed Ratio (TSR) of 0.6, the power coefficient increased by 15%, from 0.33 to 0.38. Further optimization was achieved by adjusting the blade twist angle, with an increase in power coefficient up to an optimal angle of 45°, beyond which efficiency declined. Implementing a two-stage turbine setup with a 90-degree phase difference between stages further improved the power coefficient to 0.51 at the same TSR. Additionally, the use of deflectors, particularly at a 90° angle, significantly boosted the power coefficient, highlighting their effectiveness in optimizing water flow impact on the turbine. This comprehensive study not only advances the understanding of Savonius turbine optimization but also contributes to broader renewable energy applications. The research offers critical insights into sustainable hydroelectric power generation, providing practical solutions to enhance turbine performance for real-world applications. © 2024

关键词： Clean energy

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A Hybrid Approach to Dynamic Thread Allocation Driven by Workload and Task Prioritization

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SN computer science 2025年第3期6卷 1-19页

作者： Abirami, K. Harini, N. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

In today’s rapidly changing world, cloud service providers face numerous challenges in managing resources and meeting customer demands. To address these challenges, cloud service providers should prioritize the tasks and allocate threads efficiently. By allocating threads efficiently, service providers can optimize resource utilization and reduce response times and job completion times which in turn improves customer satisfaction. We propose a hybrid model that integrates the threshold-driven thread Balancer (TDTB) for dynamic thread allocation with a Customer Retention Model (CRM) to prioritize tasks based on customer behaviour. Our model initiates the establishment of a thread pool configured with a dynamic threshold based on workload (on-demand requests). Concurrently, a queue to handle incoming customer requests is set up alongside a CRM priority queue designed for task prioritization. Our model calculates Job duration and completion times for n priority classes. Crown consumers experience shorter durations and completion times compared to other priority classes. Tasks are completed in descending order of priority. The results of thorough experimentation clearly show that crown consumer tasks are highly satisfied. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Customer segmentation Response time Service differentiation Thread allocation Throughput Workload

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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CryptStego: Powerful Blend of Cryptography and Steganography for Securing Communications

CryptStego: Powerful Blend of Cryptography and Steganography...

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International Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

作者： Pandey, Shraiyash Baniya, Pashupati Nand, Parma Hameed, Alaa Ali Bhushan, Bharat Jamil, Akhtar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9783031567278

In today’s era, security is one of the most critical issues in the development of electronic communications applications, especially when sending private data. The data may be encrypted with several algorithms;however, an extra layer of security can improve protection by a significant amount. Therefore, in this paper, we have developed an application, CryptStego, to secure data using two techniques, cryptography and steganography, to transmit data securely. The encryption of original data is executed using Blowfish algorithm, a cryptographic technique. Additionally, the encrypted data is hidden through using Least Significant Bit (LSB), a steganography technique. The implementation of both techniques offers an extensive level of security, since an intruder must firstly identify the encrypted text within the image to attain the encrypted text, then secondly to decrypt using the algorithm to obtain the original message. Therefore, any intruder must encounter multiple levels of security to obtain the original message from the cipher image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Steganography

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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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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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