检索结果-内蒙古大学图书馆

Jellyfish Search Optimization with Deep Learning Driven Autism Spectrum Disorder Classification

computers, Materials & Continua 2023年第1期74卷 2195-2209页

作者： S.Rama Sree Inderjeet Kaur Alexey Tikhonov E.Laxmi Lydia Ahmed A.Thabit Zahraa H.Kareem Yousif Kerrar Yousif Ahmed Alkhayyat Department of Computer Science&Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Computer Science&Engineering Ajay Kumar Garg Engineering CollegeGhaziabadUttar PradeshIndia Department of Human Resource Management Moscow Aviation InstituteMoscowRussia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Computer Communication Engineering Al-Rafidain University CollegeBaghdadIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq College of Technical Engineering The Islamic UniversityNajafIraq

Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and *** ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with *** of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were ***,healthcare and machine learning(ML)industries are combined for determining the existence of various *** article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)*** goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical *** proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform *** addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature ***,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ***,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU *** experimental assessment of the JSODL-ASDDC model is investigated against distinct *** experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.

关键词： Autism spectral disorder biomedical data deep learning feature selection hyperparameter optimization data classification machine learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid Routing Identification in Wireless Sensor Networks using Convolutional Neural Networks and Random Forest 4

Hybrid Routing Identification in Wireless Sensor Networks us...

引用

4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Praveen, R.V.S. Jyothirmaye, Silumula Suresh Kumar, K.R. Umapathi, N. Manikandan, N. Mohaideen, A. Digital Engineering & Assurance Hyderabad India Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Telangana India M S Ramaiah Institute of Technology Department of Information Science and Engineering Bangalore India Jyothishmathi Institute of technology and Science Department of Electronic and Communication Engineering Karimnagar India Ssm Institute of Engineering and Technology Department of Eee Dindigul India Anna University Department of Information and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Numerous societal domains have benefited from the extensive deployment of Wireless Sensor Networks (WSNs), such as educational institutions, agriculture, manufacturing facilities, environmental monitoring, and many more. Unfortunately, because the sensor nodes are wireless, it is not possible to change their batteries while are positioned in an unattended or isolated area. Consequently, a great deal of research has been documented with the aim of increasing the node's longevity. While cluster-based routing has solved this issue to a large extent, it may be even better if key considerations were taken into account while selecting the cluster head (CH). Preprocessing, feature selection, and model training make up the suggested system. In the first of two phases of data preprocessing, and check the data for errors and completeness;if any are present, then fill in the gaps. Repairing out-of-the-ordinary data should follow the second step of locating the incorrect data. With principal component analysis (PCA), we can minimize the dataset's dimensionality while preserving the majority of the original variability;this is achieved during feature selection. It used the CRFB model for training, and the resulting model had an impressive accuracy of 92.32%. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

K-Test: An Analytical Model for Measurement-Based Server Selection Based on Response Time

引用

IEEE Transactions on Vehicular Technology 2023年第2期72卷 2338-2351页

作者： Sou, Sok-Ian Bhooanusas, Nuntanut Lin, Yi-Bing Deng, Der-Jiunn National Cheng Kung University Department of Electrical Engineering Institute of Computer and Communication Engineering Tainan City701 Taiwan National Chiao Tung University Department of Computer Science Hsinchu Taiwan National Changhua University of Education Department of Computer Science and Information Engineering Changhua500 Taiwan

Different paradigms employ server selection to establish a fast connection to end users, thereby reducing the delay associated with exchanging data between mobile users and servers. To efficiently infer the statistics of the server response, we propose an analytical model for server selection based on K measured response times, namely, the K-Test method. First, the probability that the user device correctly selects the best server and the corresponding time taken in K-Test method are investigated. Then, we study the server switch when connection failure is encountered. The proposed analytical model for K-Test method considers the distribution of the response time of candidate servers and derives three metrics, namely the probability of selecting the best server, the expected latency of the server selection process, and the false connection failure detection probability. The analytical model is validated by the simulation. Numerical results show that both the value of K and the number of candidate servers significantly affect the selection probability. Also, it needs more test rounds to correctly find the best server when users experience a variation in the network response times. © 1967-2012 IEEE.

关键词： Analytical models

来源：评论

学校读者我要写书评

暂无评论

Non-proliferative Diabetic Retinopathy(NPDR) using Deep learning approach 15

Non-proliferative Diabetic Retinopathy(NPDR) using Deep lear...

引用

15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Reethika, A. Priyadharsini, S. Sheetal, Attar Mahay Nivethitha, T. Kavitha, N. Vijaykumar, G. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India CMR Institute of Technology Department of Computer Science and Engineering Bangaluru India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Electronics and Instrumentation Engineering Coimbatore India Sri Shanmugha College of Engineering & Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350370249

Non-proliferative diabetic retinopathy (NPDR), a frequent eye problem in diabetics, can result in vision loss. For prompt treatment and prevention, early detection is essential. Promising outcomes in NPDR detection have been observed in recent breakthroughs in computer vision and deep learning systems. In order to detect NPDR utilizing retinal fundus pictures, a unique deep learning-based approach is proposed in this study. The approach employs a fully connected layer for classification and a pre-trained convolutional-neural-network for extraction of feature. The algorithm performs better than state-of-the-art techniques by accuracy, specificity, sensitivity, and under the (ROC) receiver operating characteristic curve, according to experiments conducted on a publicly accessible dataset. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Assessing Social Media's Impact on Mental Health: A Gaussian Naive Bayes Approach 4

Assessing Social Media's Impact on Mental Health: A Gaussian...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Nanditha, S. Joy, Helen K. Kounte, Manjunnth R. Sridevi, R. Jayarani, A. Electa Alice Vanusha, D. Christ University Department of Computer Science Bangalore560029 India School of Electronics and Communication Engineering Reva University Bangalore India Ks Institute of Technology Department of Electronics and Communication Engineering Bangalore India Srm Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387490

In recent times, social media has gained immense popularity. However, there is a growing concern among individuals regarding its impact on mental well-being. Extensive research has been conducted to unravel the intricate connection between the use of social media and mental health. This study thoroughly examines the existing literature by incorporating various studies that cover topics such as machine learning for analysing depression-related sentiments and the effects of social media on specific demographic groups. The aim is to develop a comprehensive understanding of the relationships between these factors. In order to gain a deeper understanding of mental health indicators, a dataset consisting of 481 records was analysed using various techniques including pre-processing, correlation analysis, data visualisation, and Gaussian Naive Bayes predictive modelling. Based on our findings, it appears that individuals who engage with social media may experience negative impacts on their mental well-being. During our investigation, we conducted tests on different machine learning models and discovered that Gaussian Naive Bayes yielded the highest level of accuracy. The presence of multicollinearity among independent variables emphasises the importance of being cautious when interpreting research findings. This study makes a valuable contribution to our understanding of the complexities involved in using social media platforms while managing mental health issues. By focusing on the intersection of technology and psychiatric illnesses, it lays the groundwork for future research and interventions in this important area. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

ENERGY EFFICIENT SECURITY PROTOCOL FOR HEALTHCARE INTERNET OF THINGS

引用

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第12期83卷 65-78页

作者： Babu, Ravi Mahesh Malluvalasa, S.N. Lakshmi Devi, M. Kalpana Karthik, Sagirala Satya Rakesh, M. Prasad, Satamraju Krishna 522508 India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering Department of Computer Science and Engineering Newton’s Institute of Engineering Guntur Andhra Pradesh Macherla India

The Energy Efficient Security for Internet of Things (EESP-IoT) protocol aims to achieve a harmonious equilibrium between energy preservation, security, and network optimization within the ever-changing domain of the internet of things (IoT). EESP-IoT coordinates the actions of IoT devices where each device independently optimizes its energy usage throughout the network to ensure stability and equity. Adaptive techniques such as power optimization, alternating between sleep and wake modes, and energy harvesting reduce energy inefficiency, while assuring security by using lightweight specifically designed cryptographic techniques. By including energy and security considerations, the protocol achieves both efficiency and resilience against a range of security threats. EESP-IoT exhibited exceptional energy conservation skills, while maintaining comparable levels of throughput and latency. The protocol underwent testing in a simulated environment to assess its ability to handle a large number of devices and its effectiveness in various network circumstances. The findings confirmed that EESP-IoT is capable of maintaining a high degree of network efficiency and strong security procedures, while considerably prolonging the lifespan of IoT devices. © 2024 by Begell House, Inc.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Video and image quality improvement using an enhanced optimized dehazing technique

引用

Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Testing the Feasibility of Linear Programs with Bandit Feedback 41

Testing the Feasibility of Linear Programs with Bandit Feedb...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Gangrade, Aditya Gopalan, Aditya Saligrama, Venkatesh Scott, Clayton Department of Electrical Computer Engineering Boston University United States Department of Electrical Engineering and Computer Science University of Michigan United States Department of Electrical Communication Engineering Indian Institute of Science India

While the recent literature has seen a surge in the study of constrained bandit problems, all existing methods for these begin by assuming the feasibility of the underlying problem. We initiate the study of testing such feasibility assumptions, and in particular address the problem in the linear bandit setting, thus characterising the costs of feasibility testing for an unknown linear program using bandit feedback. Concretely, we test if ∃x : Ax ≥ 0 for an unknown A ∈ m×d, by playing a sequence of actions xt ∈ d, and observing Axt + noise in response. By identifying the hypothesis as determining the sign of the value of a minimax game, we construct a novel test based on low-regret algorithms and a nonasymptotic law of iterated logarithms. We prove that this test is reliable, and adapts to the 'signal level,' Γ, of any instance, with mean sample costs scaling as Õ(d2/Γ2). We complement this by a minimax lower bound of Ω(d/Γ2) for sample costs of reliable tests, dominating prior asymptotic lower bounds by capturing the dependence on d, and thus elucidating a basic insight missing in the extant literature on such problems. Copyright 2024 by the author(s)

关键词： Software testing

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Bus Tracking and Arrival Prediction in Real Time for Both Public and Private Sectors, Powered by Firebase 3

Revolutionizing Bus Tracking and Arrival Prediction in Real ...

引用

3rd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2024

作者： Shafiulla, Mohammed Abdul Lateef Haroon, P.S. Naveed, Shaik Mohammed Ahmed, Suhail Mahika Dhar, M. Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology And Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India

ISBN: (纸本)9798350318609

Public transport systems often face challenges related to reliability and efficiency, leading to inconvenience and uncertainty for passengers. Existing solutions for real-time bus monitoring typically rely on expensive Internet of Things (IoT) components. This paper presents Track N Go, a cost-effective alternative for real-time monitoring of public buses and taxis using mobile phone location data. The system utilizes a robust backend system built with Java and *** for the user interface. Integration of GPS via the Map Box API and Firebase database ensures accurate location tracking. Track N Go provides accurate real-time monitoring, addressing challenges associated with public transport and improving overall urban mobility. The system enhances convenience and peace of mind for passengers by delivering reliable and user-centric solutions. The evaluation of Track N Go demonstrates its effectiveness in improving urban mobility and transit services. By offering a reliable and cost-effective solution for real-time bus monitoring, Track N Go aims to streamline daily commutes and make public transport more efficient and predictable. © 2024 IEEE.

关键词： Urban transportation

来源：评论

学校读者我要写书评

暂无评论

An Efficient Quantum Enabled Machine Algorithm by Universal Features for Predicting Botnet Attacks in Digital Twin Enabled IoT Networks

引用

清华大学学报自然科学版（英文版） 2025年第3期30卷 947-956页

作者： Katta Rajesh Babu Naramula Venkatesh K.Shashidhar Yellampalli Dasaratha Rami Reddy K.Naga Prakash Department of ECE Koneru Lakshmaiah Education FoundationVijayawada 520002India School of Computer Science and Artificial Intelligence SR UniversityWarangal 506371India Department of Electronics & Communication Engineering Siddhartha Institute of Technology & SciencesHyderabad 500091India Department of CSE Chaitanya Bharathi Institute of TechnologyProddatur 520002India Department of ECE Seshadri Rao Gudlavalleru Engineering CollegeProddatur 520002India

In this manuscript,the authors introduce a quantum enabled Reinforcement Algorithm by Universal Features(REMF)as a lightweight solution designed to identify and assess the impact of botnet attacks on 5G Internet of Things(IoT)***'s primary objective is the swift detection of botnet assaults and their effects,aiming to prevent the initiation of such *** algorithm introduces a novel adaptive classification boosting through reinforcement learning,training on values derived from universal features extracted from network transactions within a given training *** the prediction phase,REMF assesses the Botnet attack confidence of feature values obtained from unlabeled network *** then compares these botnet attack confidence values with the botnet attack confidence of optimal features derived during the training phase to predict the potential impact of the botnet attack,categorizing it as high,moderate,low,or not-an-attack(normal).The performance evaluation results demonstrate that REMF achieves the highest decision accuracy,displaying maximum sensitivity and specificity in predicting the scope of botnet attacks at an early *** experimental study illustrates that REMF outperforms existing detection techniques for predicting botnet attacks.

关键词： botnet reinforcement learning 5G-enabled Internet of Things(IoT) network security machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：