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Placement of Charging Sites for Off-Service Battery Electric Bus in Transit Networks 8

Placement of Charging Sites for Off-Service Battery Electric...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Sobhani, Ehsan Yassine, Abdulsalam Ameli, Amir Riahinia, Shahin Department of Computer Science Lakehead University Thunder BayON Canada Department of Software Engineering Lakehead University Thunder BayON Canada Department of Electrical and Computer Engineering Lakehead University Thunder BayON Canada

ISBN: (纸本)9798350382150

Battery electric buses (BEBs) have received significant recognition as an environmentally conscious and sustainable means of transportation. Placement of charging stations to ensuring efficient and consistent charging of BEBs is a significant challenge in an electrified public transit network. This paper introduces the Constrained Greedy Clustering (CGC) algorithm, a data-driven approach for hierarchically determining optimal charging placement locations. The algorithm aims to minimize energy consumption and optimize charging load distribution in transit networks using clustering techniques. This improves the placement of charging sites for off-service BEBs in transit networks, aiming to minimize energy consumption and balance the charging sites’ load. ©2024 IEEE.

关键词： Energy efficiency

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Tech Meets Agriculture - Precise Plant Disease Detection Using Deep Learning Algorithm

Tech Meets Agriculture - Precise Plant Disease Detection Usi...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Arunasakthi, K. Akilandeswari, V. Shalini, K.S. Archana, K. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Plant disease prediction is crucial aspect of agricultural field. Plant diseases are one of the major concerns for agriculture in recent times;most of the crops are prone to damage caused by these diseases, which later hampers global food security. The traditional methods used for disease analysis rely strictly on visual inspection. These methods are time-consuming and prone to errors. Deep learning has therefore emerged as a significant and formidable instrument for the automation of plant disease detection. This project uses the deep learning models, YOLOv5 and YOLOv9. It ranks high on accuracy and ensures efficiency in the system to detect diseases related to plant leaves. It uses annotated datasets for several plant leaf diseases with a convolutional neural network to extractfeatures and enable classification. The system provides real-time automated solutions for the identification of plant diseases through the integration of deep learning models into agricultural practices, with a view to allowing growers to take timely action to avoid further damage to crops. The proposed technique not only heightens the rate of accuracy for disease detection but also contributes toward the growing dimension of precision agriculture, within which technology acts as an assistant in the optimization of crop management and enhancement of yield. © 2024 IEEE.

关键词： Convolutional neural networks

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High-Resolution Data-Driven Approach for Pedestrian Detection Using Deep Learning 2

High-Resolution Data-Driven Approach for Pedestrian Detectio...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Selva Lakshmi, C.B. Dharshanaa, S. Dhanavidhya, C. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350349221

Pedestrian detection is one of the primary objectives in the field of computer vision demonstrating versatile applications in various domains including pedestrian monitoring in surveillance systems and driverless automobile assistance. Since the surveillance cameras are set high to capture a wide range of areas, they produce small and noisy images. When these footage are given as input, the extracted features of the pedestrian images produce an inferior detection rate. To address the above issue, this research suggests a solution employing stationary wavelet dilated residual (SWDR) networks that significantly enhance edge details of the low-resolution images thus improving the efficiency of the pedestrian detection system. © 2024 IEEE.

关键词： Wavelet transforms

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Leveraging Large Language Models and RAPIDS AI to Detect Fraudulent AI-Generated Marketplace Reviews

Leveraging Large Language Models and RAPIDS AI to Detect Fra...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Dudeja, Nishit Pundir, Arun Singh Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350350821

Consumer confidence is, in the present time, a dilemma given the steadily rising number of deceptive and inaccurate AI-generated reviews on internet marketplaces. There is an urgent need for a thorough dataset, which is nowhere to be found, to address the pervasiveness of fraudulent reviews. In order to close this gap, in this work, we propose relevant detection techniques using GPU-accelerated models from the RAPIDS AI library and new age Large Language Models like GPT-2 and BERT. By carefully constructing a unique dataset of about 54,000 reviews, we have combined human-authored reviews gleaned from web scraping with AI-generated reviews produced with the Chat-GPT 3.5 model, which supports the representative spectrum across all review polarities and offers a solid basis for precise analysis. We show significant accuracy in differentiating between reviews written by humans and those produced by AI and provide an all-inclusive solution to this new issue by using sophisticated LLMs and RAPIDS AI models. Moreover, we also compare the approaches to find the most successful one. This approach not only tackles the problem of fraudulent reviews but also establishes a new benchmark for keeping online marketplaces morally sound. © 2024 IEEE.

关键词： Marketplaces

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RTIDS: A Robust Transformer-based Approach for Intrusion Detection System 10

RTIDS: A Robust Transformer-based Approach for Intrusion Det...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Saranya, N. Abbinavu, T. Kumar, P. Arun Gnanasekar, R. Prasanth, P. Guru Subha, R. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

Intrusion Detection Systems (FIDSs) with foamy disturbance are crucial for preventing security breaches in the constantly growing world of the internet's interconnectivity across many domains. Among the many applications are supply chains, manufacturing, smart homes, smart grids, healthcare, and environmental tracking. Traditional intrusion detection methods, while effective in conventional contexts, require adaptation and enhancement to cater to the unique challenges, including devices with limited resources, memory, and battery capacity, as well as particular protocol stacks. In this study, we develop a lightweight attack detection method that detects and stops attackers from injecting superfluous data into the network using a supervised machine learning-based FIDS. Our simulation results demonstrate that the suggested FIDS- based classifier works well in terms of classification accuracy and detection speed when it is enhanced by a combination of two or three complex features. © 2024 IEEE.

关键词： Network intrusion

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A Probabilistic Reasoning Framework to Detect Fake News on Social Media 19

A Probabilistic Reasoning Framework to Detect Fake News on S...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Rashid, Mehreen Anber, Fabliha Samiullah, Md Ahmed, Chowdhury Farhan Leung, Carson K. Pazdor, Adam G.M. Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh Department of Computer Science University of Manitoba WinnipegMB Canada

ISBN: (纸本)9798331507817

The rapid growth of social media has made the Internet a critical platform for spreading misinformation, which shapes public opinion and harms society. Despite significant research in fake news detection, most probabilistic efforts rely heavily on Naive Bayes, with limited exploration of other probabilistic models. This paper introduces a Bayesian network (BN) modelling-based framework for fake news detection, offering a probabilistic estimate of the likelihood of news being false. Unlike binary classification, this approach reflects human decision-making by evaluating three key questions: "who", "what", and "when". Each module corresponds to a specific feature set, enabling nuanced reasoning about the news’s credibility. The framework is flexible, allowing adjustments through expert input, knowledge bases, or real-world data. We validate the approach using a semi-synthetic dataset containing features from news content, user behaviour, and social context. The results highlight the framework’s capacity to leverage expert knowledge, providing a more reliable and adaptive solution compared to traditional classifiers. The BN-based method demonstrates enhanced robustness, positioning it as a promising tool for tackling misinformation in an evolving digital landscape. © 2025 IEEE.

关键词： Bayesian network Credibility Fake news Information management Social media

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Revolution in agriculture sector using blockchain technology 4th

Revolution in agriculture sector using blockchain technology

引用

4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Saleem, Abdul Latif Paramesha, K. Vinay, M.G. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysore India

ISBN: (纸本)9781032426853

In recent years, blockchain technology has gotten a lot of attention. The introduction of blockchain smart contracts which enable the implementation of decentralized apps in trust-free contexts is one explanation for this new trend. As smart contracts become more widely used attacks exploiting their flaws will surely increase. Several ways have been investigated to mitigate these assaults and avoid breaches such as documenting vulnerabilities or model testing using formal verification. These methods however fall short of capturing the blockchain and user behaviour features. Agriculture is gradually becoming into a significant financial industry with global implications. Real-time monitoring of the weather and the condition of the soil, along with improved food quality, are what are driving the technological, ecological, and economical trend. The study explores the exciting notion of fusing smart agriculture with smart contract technology to produce not only better agricultural products but also better supply chains and agricultural logistics, resulting in a variety of advantages for all parties involved. With the use of numerical and possibly categorical data, the emphasis is on calculating similarity metrics for tuples that reflect soil and climate conditions. In addition, Solidity a high-level language for creating smart contracts meant for the Ethereum Virtual Machine is provided with a sample implementation of one such measure. The final stage for smart contracts that depend on physical objects is to highlight elements of agricultural asset digitalization. © 2024 The Author(s).

关键词： Smart contract

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Towards Enhancing Child Safety: A Deep Learning Approach to Detect Child Safe and Unsafe Objects

Towards Enhancing Child Safety: A Deep Learning Approach to ...

引用

2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Khan, Fairuz Areefin Dey, Ashim Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong4349 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331535476

The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial Intelligence (AI) age, the future belongs to domestic robots;they will be used for child education, surveillance, and healthcare support. Early identification of unsafe objects and intervention by these next-generation social family robots can play an essential role in providing a safer environment for children. Keeping these issues in mind, this study aims to tackle daily risks and accidents that endanger children by using computer vision to detect unsafe objects. To achieve this goal, a unique dataset namely ChildSUn is introduced containing approximately 5350 images of child-safe and unsafe objects of ten categories. Various CNN architectures, including custom CNN, VGG16, VGG19, ResNet50, Inception-V3, EfficientNet-B0, and MobileNetV2, are implemented on the prepared dataset. A deep dive analysis is performed on the implemented models. The EfficientNet-B0 model showed exceptional performance, reaching a remarkable level of accuracy of 99.63% on the test dataset. These findings underscore the potential of EfficientNet-B0 as an effective solution for real-world child safety applications and encourage further exploration of deep learning techniques to enhance child protection globally. We hope this work will pave the way for future social household robot development, which will ensure child safety in any place. © 2024 IEEE.

关键词： Deep learning

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High Dynamic Range Edge Filtered LeNet based Gemstone Classification

High Dynamic Range Edge Filtered LeNet based Gemstone Classi...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Devi, M. Shyamala Shaarmi, G. Priyadharshini, R. Priyanka, D. Sathvika, M. Prabavathi, R. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350374933

The precise detection of subtle characteristics and textures in gemstone photographs is necessary for the hard process of effective gemstone categorization. This research provides a unique method of classifying gemstones using convolutional neural networks (CNNs) based on LeNet and edge-filtered images. By highlighting the edges and contrasts in gemstone images, edge filtering improves the visibility of important characteristics including cuts, facets, and defects. The HDR function reveals subtle color and texture changes that are essential for differentiating between different types of gemstones by capturing a wider range of light intensities. This paper proposes High Dynamic Range Edge Filtered LeNet (HDEF-LeNet) that categorizes the Gemstone types with high accuracy. The Gemstone Dataset that includes 800 images was used for this execution. The HDR edge filtered images are extracted from the original images. After that, a customized HDEF-LeNet architecture designed to take into account the special qualities of gemstone images that is fed with this preprocessed data. Our strategy seeks to outperform conventional approaches based on conventional imaging techniques in terms of gemstone classification and accuracy. The findings of the experiments show that combining edge filtering and HDR with LeNet improves performance in classification tasks for gemstones, providing a viable approach to more accurate and consistent gemstone identification. The experiment demonstrates that the suggested HDEF-LeNet model works better in the categorization of gemstone types, with a high accuracy of 99.68%. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Powered Fungal Detection In Dried Chilli: A Deep Learning Approach 4

AI-Powered Fungal Detection In Dried Chilli: A Deep Learning...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Indira Priyadharshini, M. Pavithra, R. Mahalakshmi, M. Mohanapriya, S.U. Jasiya Yasika, S. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354218

The project aims to create an automated system for detecting fungal contamination in dried red chilies using advanced deep learning algorithms such as Convolutional Neural Network (CNN), Visual Geometry Group (VGG16), and Residual Neural Network (ResNet). Fungal contamination poses a significant threat to the quality and safety of food products, and its early detection is crucial for ensuring consumer health and preventing economic losses in the agricultural sector. To train and evaluate the performance of the CNN, VGG, and ResNet models, the proposed system uses a dataset that includes custom images of dry red chilies, both contaminated and uncontaminated. The CNN is used to extract spatial features, whereas the VGG model is known for its depth and efficacy in image recognition tasks. The proposed system employs a dataset comprising custom images of dry red chilies, both contaminated and uncontaminated, to train and evaluate the performance of the CNN, VGG, and ResNet models. The CNN is used for its ability to extract spatial features, and the VGG model is known for its depth and effectiveness in image recognition tasks. Additionally, ResNet is employed to capture temporal dependencies in the dataset, considering the potential evolution of contamination over time. The study involves a comprehensive comparison of the output accuracy of these three algorithms, considering metric accuracy. The findings from this research contribute to the development of a robust and reliable automated system for the food industry, facilitating timely and accurate identification of fungal contamination in dry red chilies. This work aims to assist in the enhancement of food safety measures and the overall quality control processes in the agricultural and food processing sectors. © 2024 IEEE.

关键词： Convolutional neural networks

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