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8th IEEE Inter.a.ional Conference on Edge Computing and Communications, EDGE 2024

作者： Huang, Yuyang Zhar.a., Faishal Hendrawan, Hendrawan Gunawi, Har.a.i S. Jiang, Junchen University of Chicago Department of Computer Science Chicago United States Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350368499

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since impor.a.t infor.a.ion may not appear.a.l the time. Consequently, each edge AI application's bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to r.a.h high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application's *** short paper explores a new accuracy-driven appr.a.h to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application's accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciErGE, which exposes a simple yet efficient inter.a.e to estimate each application's sensitivity to a small change in its bandwidth *** run CONCIErGE on state-of-the-art video-analytics applications with r.a. video str.a.s and show its early promise in gr.a.ly improving the inference accuracy of video analytics. © 2024 IEEE.

关键词： Bandwidth

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Sentiment Polarity Analysis of Bangla Food reviews Using Machine and Deep Learning Algorithms 3

Sentiment Polarity Analysis of Bangla Food Reviews Using Mac...

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3rd Inter.a.ional Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Amin, Al Sar.a., Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md robiul Hoque, Md Mahmudul Uttara University Department of Computer Science and Engineering Dhaka Bangladesh National Institute of Technology Silchar India University of Oslo Department of Informatics Oslo Norway Maharishi International University Department of Computer Science Iowa United States CCN University of Science & Technology Department of Computer Science and Engineering Comilla Bangladesh

ISBN: (纸本)9798350388282

The Internet has become an essential tool for people in the modern world. Humans, like all living or.a.isms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate appr.a.h for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024 IEEE.

关键词： Sentiment analysis

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Deadline-aware and energy efficient IoT task scheduling using fuzzy logic in fog computing

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Multimedia Tools and Applications 2025年第15期84卷 14359-14386页

作者： Thakur, rahul Sikka, Geeta Bansal, Ur.a.hi Giri, Jayant Mallik, Saurav Department of Computer Science and Engineering Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Environmental Health Harvard T H Chan School of Public Health BostonMA United States Saveetha University TN Chennai602105 India

Fog computing is an emerging paradigm that extends cloud computing (CC) by providing computation, communication, and storage services at the edge of a network, closer to end devices. It has gained significance due to the rapid development of IoT devices, which generate various types of tasks. Processing these tasks in the cloud can strain its infrastructure and lead to delays in time-sensitive requests. To address this limitation, fog computing (FC) concepts were introduced in 2012 by Cisco. FC is not meant to replace CC but rather to complement and extend its capabilities. One of the challenges in FC is efficiently assigning tasks to appropr.a.e resources to minimize makespan, energy consumption (EC), and incr.a.e the number of deadline-satisfied tasks. In this work, the improvement of semi-greedy algorithm has been done by incorporating fuzzy logic (FL). By leveraging FL, the aim is to enhance the algorithm's decision-making process and make it more adaptive to varying conditions and uncer.a.nties in the fog environment. The use of FL allows more nuanced and flexible task scheduling (TS) decisions based on fuzzy sets and fuzzy rules. The simulation experiments demonstrate that the proposed algorithm outperforms PSG (Priority-aware Semi-Greedy) and PSG-M (PSG with multistart), which were identified as the best scheduling algorithms (Algos) in the literature review. The algorithm exhibits better perfor.a.ce in terms of reducing makespan, EC, and incr.a.ing the percentage of deadline-satisfied tasks compared to PSG and PSG-M. The inclusion of FL further enhances the algorithm's effectiveness in handling complex scheduling scenarios in a FC environment. To evaluate the perfor.a.ce of the proposed algorithm, different simulation experiments have been conducted using a selected simulator.a.ter.a.systematic review of existing simulators. The experiments involved 300 and 500 random and static tasks, as well as 60 fog nodes in the fog environment. All simulations were impl

关键词： Fog computing

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Optimizing Flight routes for Better Fuel Efficiency and Optimal Path using Grover's Algorithm & QAOA Algorithm - Quantum Computing

Optimizing Flight Routes for Better Fuel Efficiency and Opti...

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2024 Inter.a.ional Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Khan, M. Imran Govindharaj, I. Sathishkumar, r. Jenifer, D. Maeghasree, T. Yohini, K. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331510022

Optimization of flight routes is one of the hard problems in the aviation sector. This paper focuses on the prospects of quantum computing developed using Grover's Algorithm and the Quantum Approximate Optimization Algorithm (QAOA). Grover's Algorithm provides a quadratic speedup for searching an unsorted database and is therefore quite useful for identifying optimum flight routes amongst millions of choices. Preliminary benchmarking suggests that it can efficiently search a space of more than one million routes in far less time than the conventional appr.a.h, which is impor.a.t for timely decisions over.a.iation. Simultaneously, QAOA refines candidate routes with the aid of r.a.-time inputs such as weather.a.d air traffic, and each time delivers near-optimal solutions even during sudden changes. The quantum framework in development would make it possible to improve the accuracy of routes recommended and respond fast to changing circumstances. This includes weather conditions whereby, based on research, estimated savings in fuel by optimizing routes run between 5% to 10%, which translates to costs lower with fewer carbon emissions playing a role in environmental sustainability. Flight scheduling also improves through quantum algorithms because of incr.a.ed operational efficiency, which would enable airlines to cope better with complicated constraints and not allow delays when flights are involved. In summary, the Grover's Algorithm and QAOA hybrid shows a more holistic appr.a.h of giving an advantage regarding speed over the traditional appr.a.h as well as flexibility. © 2024 IEEE.

关键词： Air transpor.a.ion

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber.a.tacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sar.a.e Pedda Venkata Subba rao Kavitha Thandapani Pasupuleti rama Mohan Thangam Somasundaram Yousef Far.a.ui Department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India Department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India Department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India Department of Computer Science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-br.a.ing prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and infor.a.ion *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is incr.a.ed up to 99.5%with precision and AUC of 99%for.a.l smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Textile Defect Classification Using Deep Convolutional Neural Networks (DCNN) 5th

Textile Defect Classification Using Deep Convolutional Neura...

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5th Inter.a.ional Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Potluru, Sri Sashank Kar.a.i, Tarun Mande, ramesh Gudavalli, Hruthi sri Miriyala, Sai Manikanta Department of Information Technology V R Siddhartha Engineering College Vijayawada India Department of Computer science SRK Institute of Technology Vijayawada India

ISBN: (纸本)9789819767311

The textile industry has a standing history and offers a diverse range of products, such as clothing, home furnishings, and more. The quality of the product is undeniably impacted by the fabric’s quality. The caliber of the merchandise is undeniably impacted by the fabric’s quality. Still, several complications can sur.a.e during the fabric production process due to factors, like imperfect yarn or mechanical glitches. Neglecting these flaws can incur losses and harm the brand’s reputation. At times, researchers have put forth machine learning techniques, like CNN, rCNN, and others to detect defects in fabrics. However, there is still a challenge in achieving time and accurate segmentation and classification of these defects. This research paper introduces an new appr.a.h that combines the power of DCNNs with the efficiency of the YOLOv7 detection mechanism to bridge this gap. We propose a network architecture that includes preprocessing feature extraction using resNet-50 and defect detection using YOLOv7 and then classify the fabric defect into their category using a deep convolutional neural network. This results in classification of fabric defects. Impressively, this network achieves an accuracy of 96.29%. By conducting this study, we hope to raise the bar for fabric defect detection and offer the textile industry a reliable, automated, and extremely precise instrument for ensuring fabric quality. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental Science and Pollution research 2024年第42期31卷 54525-54533页

作者： rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s perfor.a.ce. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai river, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alter.a.e framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Ver.a. GmbH Ger.a.y, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Perfor.a.ce Evaluation of Modified YOLOv5 Object Detectors for Crop-Weed Classification and Detection in Agriculture Images

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SN computer Science 2025年第2期6卷 1-12页

作者： Sonawane, Sandip Patil, Nitin N. Department of Computer Engineering R. C. Patel Institute of Technology Shahada Road Maharashtra Shirpur 425405 India Department of Computer Engineering Shri Vile Parle Kelavani Mandal’s College of Engineering Maharashtra Shirpur 425405 India

Crop-weed classification and detection are essential tasks in precision agriculture for effective weed management. In this study, we employed the most popular YOLO (You Only Look Once) object detection algorithm to address this challenge. A Weed1000 dataset comprising 1228 annotated images was used for training and evaluation. Various versions of the YOLOv5 algorithm, namely YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5X, were implemented and evaluated with reference to perfor.a.ce evaluation metrics including precision, recall, F1 score and mean Average Precision (mAP) at different Intersection over Union (IoU) thresholds. The hyper-parameter tuning and addition of layers in the modified YOLOv5X demonstrated higher precision of 95.3%, recall of 92%, F1 score of 93% and mAP of 94.5% and 47.8% at 0.5 and 0.95 IoU thresholds, respectively. Furthermore, we developed a web application using Flask to deploy the YOLOv5 model, enabling r.a.-time crop-weed detection accessible via a user-friendly inter.a.e. We have also compared our proposed appr.a.h with previous studies. Notably, this research introduces novel modifications to the YOLOv5 algorithm to enhance its effectiveness and highlights the practical deployment of such models for.a.ricultural applications. The findings underscore the impor.a.ce of model selection and deployment strategies in achieving accurate and efficient crop-weed detection systems. Additionally, our modifications to the YOLOv5 algorithm and the development of a user-friendly deployment application represent significant advancements in the field. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： computer vision Image processing Machine learning Precision agriculture robotics weeding

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Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 1027-1042页

作者： Swar.a.Sudha Muppudathi Valar.a.hi Krishnasamy Department of Computer Science and Engineering Ramco Institute of TechnologyRajapalayam626117India Department of Electronics and Communication Engineering P.S.R.Engineering CollegeSivakasi626140India

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to r.a.h a group of *** use of SNS often inﬂicts the physical and the mental health of the ***,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative inﬂ*** state-of-art Natural Language processing techniques for.a.omaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as cer.a.nly do not guarantee maximum *** overcome these issues,the proposed methodology utilizes a Modiﬁed Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky rectiﬁed Linear Unit(LreLU).Here,each word in the social media text is analyzed based on its *** stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overﬁ*** LreLU overcomes the high computational cost and gradient vanishing problem associated with other.a.tivation *** also doesn’t stop the learning process when the values are *** MCNN computes a speciﬁed score value using a novel integrated anomaly detection *** on the score value,the anomalies are identiﬁed.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modiﬁed CNN and fast convergence is *** this way,the perfor.a.ce of the model is enhanced in terms of classiﬁcation *** efﬁciency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,speciﬁcity,recall,and *** proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter.a.d reddit respectively.

关键词： Anomaly detection convolutional neural network social networking sites stochastic pooling teacher learner-based optimization

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Deep-Q-Based reinforcement Learning Method to Predict Accuracy of Atari Gaming Set Classification

Deep-Q-Based Reinforcement Learning Method to Predict Accura...

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2023 Inter.a.ional Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Gobinathan, N. Ponnusamy, r. V.R.S. College of Engineering and Technology Department of Computer Science and Engineering India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348910

A pivotal acquisition model designed to formulate control strategies directly from high-level perceptual data using reinforcement learning is introduced by us. The training of this model, a convolutional neural network, was done using a different Q-learning algorithm. An output is produced in the form of a value function that indicates anticipated future earnings after processing raw image inputs. recent demonstrations of cutting-edge perfor.a.ce in reinforcement learning tasks have been made by these neural networks, which leverage Deep Q-Networks (DQNs) based on the Q-Learning algorithm. The planning process for DQNs is significantly different from that of standard deep neural networks, resulting in significant challenges in specific reinforcement learning scenarios. © 2023 IEEE.

关键词： reinforcement learning

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