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An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm

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Journal of Computing science and engineering 2023年第3期17卷 127-134页

作者： Sachnev, Vasily Mahanand, B.S. School of Information Communication and Electronics Engineering Catholic University of Korea Bucheon Korea Republic of Department of Information Science and Engineering Sri Jayachamarajendra College of Engineering JSS Science and Technology University Mysuru India

In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods. © 2023. The Korean Institute of information Scientists and Engineers

关键词： Genetic algorithms

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Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

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computer Systems science & engineering 2023年第2期44卷 1451-1466页

作者： A.Jameer Basha S.Aswini S.Aarthini Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of ComputationalMathematics Science and Engineering(CMSE) Michigan State University East LansingMI48824USA.

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart *** power is one of the most signiﬁcant resources in *** enhancing a power factor,the clustering techniques are *** the forward of data in WSN,more power is *** the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and *** the existing system,it does not deal with end-to-end delay and deliv-ery of *** overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is *** Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness *** Swarm Optimization(CSO)helps to solve the complex opti-mization ***,it consists of chickens,hens,and *** divides the chicken into *** Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the *** proposed GA-CSO with LBCM improves the life-span of the ***,it minimizes the energy consumption and also bal-ances the load over the *** proposed method outperforms by using the following metrics such as energy efﬁciency,ratio of packet delivery,throughput of the network,lifetime of the sensor ***,the evaluation result shows the energy efﬁciency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

关键词： Energy efﬁciency sensor nodes chicken swarm optimization load balanced clustering method wireless sensor network cluster heads load-balancing ﬁtness function

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of information technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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From Blackboards to Byteboards: Adapting to the Digital Age in Undergraduate Education on Campus 17

From Blackboards to Byteboards: Adapting to the Digital Age ...

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17th International Conference on Signal-Image technology and Internet-Based Systems, SITIS 2023

作者： Anannya, Tasmiah Tamzid Mahmud, Sharifa Rania Khan, Shahriar Rahman Zaman, Syed Rohit Jalal, Md. Shah Sharmin, Sadia Bangladesh University of Engineering and Technology Department of Computer Science and Engineering Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Bangladesh American International University Department of Computer Science Bangladesh

ISBN: (纸本)9798350370911

In the current educational landscape, maintaining student engagement is a persistent challenge. This paper addresses this issue and investigates the potential of attention engineering as a solution to enhance engagement among undergraduate students. The main goal is to investigate the use of attention engineering techniques in undergraduate education to find effective methods and measure their impact on student engagement and academic performance. This research conducts a thorough literature review to assess the feasibility and effectiveness of these techniques in undergraduate classrooms, exploring the utilization of personalized content, interactive tools, and data-driven insights to maintain student attention. To improve attention and learning, several technology tools, and strategies such as online videos and tutorials, interactive multimedia content, gamification, flipped learning, social network tools, and Learning Management Systems (LMS) are studied. This research addresses three key research questions, the impact of technology integration and interactive activities on student engagement and comprehension, effective technology-based tools for learning environments, and the influence of attention-centric teaching strategies on student learning outcomes. This research identifies focused technologies and approaches, as well as the country attaining and the most utilized technology from current works. Our research has also proposed some findings that offer valuable guidance for educators and institutions seeking to optimize teaching methods and ultimately improve the learning experience in undergraduate education. © 2023 IEEE.

关键词： Students

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CampusSight—College Management Automation Using Full Stack and RPA 12th

CampusSight—College Management Automation Using Full Stack ...

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

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, Machine Learning and Artificial Intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Sentiment Analysis of Twitter Data Using Big Data Analytics and Deep Learning Model

Sentiment Analysis of Twitter Data Using Big Data Analytics ...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Vanam, Harika Raj, R. Jeberson Retna Computer Science and Engineering Chennai India Information Technology Chennai India

ISBN: (纸本)9798350334357

People from all over the world can express their thoughts and opinions through various online social media platforms. People use social media platforms online daily to communicate with one another and stay informed about current events. A large number of tweets covering a wide range of subjects are sent to Twitter daily. Twitter is one of the most well-known and widely used online social media platforms. Extracting features and locating trends can be accomplished through the use of machine learning algorithms. Tools and strategies designed specifically for working with large amounts of data are required to successfully extract useful information from the never-ending stream of data that is produced by Twitter. In this paper, we mainly focus on hashtag identification and identify the industry that possesses the highest share of voice. In this paper, we collect live data from Twitter by using Apache Spark. After that, we classify each tweet by making use of the machine learning techniques that are provided by the Apache Spark machine learning library. To test the model, Convolution neural network (CNN) and logistic regression (LR) are being utilized. The CNN method outperformed the Logistic Regression strategy by performing with an accuracy of approximately 95% on average and scoring 0.60 on the F1 scale. Both the accuracy and the F1 score are currently sitting at 0.59. According to the findings, real-time tweets can be evaluated considerably more quickly using the Apache Spark tool for big data as opposed to the conventional execution environment. The results show that real-time tweets can be evaluated much faster using the Apache Spark tool for big data instead of the usual execution environment. © 2023 IEEE.

关键词： Sentiment analysis

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Motion Detection in Real-Time Surveillance Using Two Frame Differencing 1

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8th International Conference on information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan Mandal, Gouranga National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India Vellore Institute of Technology Andhra Pradesh India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

This research article delves into the creation of a real-time system for detecting motion, particularly for surveillance purposes. The proposed approach relies on computer vision algorithms to analyze live video streams captured by a camera. By scrutinizing these feeds, the algorithms can discern alterations within the scene, specifically identifying objects in motion. The primary aim is to ensure that this system functions in real time, thereby furnishing instantaneous insights to security personnel. The paper expounds on the different steps integral to motion detection, encompassing procedures like background subtraction, foreground segmentation, and object tracking. To validate the system’s effectiveness, an evaluation is conducted using a collection of actual surveillance videos. The findings underscore the system’s capability to accurately detect moving objects across diverse scenarios. The article concludes by contemplating the system’s potential applications, which span security setups, traffic supervision, and ecological monitoring. The focal technique utilized in this study is Frame Differencing, a means of detecting object motion. This approach is adept at distinguishing moving objects within a given environment. In conjunction with this method, the paper emphasizes the viability of background subtraction to further enhance Frame Differencing, thereby augmenting its precision and efficacy. Upon applying this method to a camera, it is discerned that luminosity has a significant impact on the minimal value. Notably, a minimal value of 35 is identified as the most favorable choice. Here, threshold value 35 is selected because we have seen in our experiment that 35 gives the optimal result with our algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cameras

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A Comprehensive Review on computer Vision and Fuzzy Logic in Forensic science Application

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Annals of Data science 2023年第3期10卷 761-785页

作者： Thakkar, Prarthi Patel, Darshil Hirpara, Isha Jagani, Jinesh Patel, Smit Shah, Manan Kshirsagar, Ameya Department of Computer Science Indus University Gujarat Ahmedabad India Department of Chemical Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India Department of Information Technology Symbiosis Institute of Technology Maharashtra Pune India

Criminalistics is another name for forensic science. It uses science in a criminal investigation as governed by judicial criteria of acceptable, relevant, and admissible evidence and criminal procedure. Forensic science has been around for a long time and has seen considerable changes, from fingerprint identification to DNA analysis and digital forensics. The study focuses on the most critical technologies in forensic science, then deconstructs numerous computer vision, image processing, and fuzzy logic methodologies in the large subject of forensic research. It also addresses the prospects for using the technology in approaches ranging from biometric identification to a 3D reconstruction of a crime scene. To some extent, adopting the numerous methodologies outlined in the paper helps overcome the disadvantages and challenges of traditional forensics procedures. Furthermore, some constraints are taken into account. For example, in various ways, the primary evidence is pre-processed and translated to an intermediate or more lucid form before the crux algorithms are applied. As a result, there is still plenty of room for research in this subject, such as developing solid algorithms, making the technology accept raw data, etc. If utilized correctly, forensic science technology has the potential to affect a paradigm shift in the criminal justice system. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fuzzy logic

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Image Representations of Numerical Simulations for Training Neural Networks

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computer Modeling in engineering & sciences 2023年第2期134卷 821-833页

作者： Yiming Zhang Zhiran Gao Xueya Wang Qi Liu School of Civil and Transportation Engineering Hebei University of TechnologyTianjin300401China School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China

A large amount of data can partly assure good fitting quality for the trained neural *** the quantity of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in engineering practice,numerical simulations can provide a large amount of controlled high quality *** the neural networks are trained by such data,they can be used for predicting the properties/responses of the engineering objects instantly,saving the further computing efforts of simulation ***,a strategy for efficiently transferring the input and output data used and obtained in numerical simulations to neural networks is desirable for engineers and *** this work,we proposed a simple image representation strategy of numerical simulations,where the input and output data are all represented by *** temporal and spatial information is kept and the data are greatly *** addition,the results are readable for not only computers but also human *** examples are given,indicating the effectiveness of the proposed strategy.

关键词： Numerical simulations neural network pre-/post-processing data compression

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Exploring Machine Learning Methods to Identify Patterns in Students' Solutions to Programming Assignments 53

Exploring Machine Learning Methods to Identify Patterns in S...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Liu, Xiaojin Castellanos, Hugo Wiese, Lucas Magana, Alejandra J. Purdue University Department of Computer Science Department of Computer and Information Technology West LafayetteIN United States Purdue University Department of Computer and Information Technology West LafayetteIN United States School of Engineering Education Purdue University Department of Computer and Information Technology West LafayetteIN United States

ISBN: (纸本)9798350336429

technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry's demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students' solutions to programming assignments over the course of a semester. With this, the goal is to characterize students' most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education. © 2023 IEEE.

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