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Utilizing Genetic-Neural System Fusion for Advanced Prediction and Classification of Damage Intensity in Road Traffic Accidents 2

Utilizing Genetic-Neural System Fusion for Advanced Predicti...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350371314

This study proposes an analysis and prediction framework for road traffic accidents (RTAs) in the hilly terrain of Himachal Pradesh and several states in, India, integrating machine learning techniques such as neural networks (NNs) and genetic algorithms (GAs). Utilizing a comprehensive dataset comprising historical accident records, geographical features, weather conditions, and demographic information specific to Himachal Pradesh, the framework aims to identify patterns and correlations contributing to RTAs. Neural networks are employed to discern complex relationships within the data, while genetic algorithms optimize model parameters for enhanced predictive accuracy. By forecasting accident-prone areas and periods, this approach enables proactive interventions and targeted road safety measures. The outcomes of this research provide valuable insights for stakeholders and policymakers, facilitating the reduction of RTAs and mitigating their impact in the challenging terrain of Himachal Pradesh including all states of India. © 2024 IEEE.

关键词： Genetic algorithms

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GNN-based Disease Detection and Classification in Cassava Leaf 5

GNN-based Disease Detection and Classification in Cassava Le...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Meenalochini, M. Amalroshini, A. Nandhini, K L Megha Murugan, C R Sogil Kongu Engineering College Dept. of Computer Science and Design Erode India

ISBN: (纸本)9798331518097

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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Blockchain Based Credit System to Increase the Creditability through Carbon Offset Level 5

Blockchain Based Credit System to Increase the Creditability...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Padmavathi, A. Reddy, G. Charan Santhosh, Gauri Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Our country has relied significantly on agriculture as its main source of income for several decades. The demand for food and crops is growing due to the growing population, which offers the agriculture sector tremendous prospects for growth. However, despite the soaring demand and bright future, India's agriculture industry has failed to demonstrate appreciable profitability. In the paper that follows, we provide a novel strategy meant to revolutionize agricultural marketing networks. Blockchain technology is included in our approach to enable the virtualization of products and carbon offset certificates. This system encourages agricultural producers to reduce carbon emissions and address environmental concerns. By utilizing blockchain in a particular way, carbon offset credits can be used to securely track, trade, and for other fostering sustainability practices. This integration of technologies assures to change how agricultural products are marketed currently. It also helps in promoting sustainability and provides consumers with all the valuable information about the environmental impact of their purchases. © 2024 IEEE.

关键词： Blockchain carbon offset credits Product visualization virtualization

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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Classification of non-invasive EEG signals during Motor Imagery tasks using a Large Language Model 23

Classification of non-invasive EEG signals during Motor Imag...

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23rd International Conference on Cyberworlds, CW 2024

作者： Lim, Do-Hyeon Cho, Myeong-Ju Kim, Hoon-Hee Pukyong National University Dept. of Computer Engineering Busan Korea Republic of

ISBN: (纸本)9798331527174

EEG data have significant academic and clinical value because of their non-invasive nature which allows for easy extraction. At the same time, they are also prone to distortion from noise, making quality interpretation challenging. Meanwhile, the advancements of Large Language Models (LLMs) have made them applicable to complex tasks. In this study, we propose a fine-tuned LLM classifier that is able to classify motor imagery EEG data. The research compares the performance of fine-tuned LLM with that of Support Vector Machine, Random Forest, and Multi-Layer Perceptron models. The results show that the fine-tuned LLM can achieve comparable performance compared to traditional machine learning methods and demonstrate the possibility that the finetuned LLM could be applied to more diverse tasks based on EEG data. © 2024 IEEE.

关键词： Electroencephalography

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The Tree Autoencoder Model, with Application to Hierarchical Data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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Fruit Identification using YOLO v8 and Faster R-CNN -A Commparative Study 2

Fruit Identification using YOLO v8 and Faster R-CNN -A Commp...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sawant, Kedar Shirwaikar, Rudresh D. Ugavekar, Raj Ravindra Chodankar, Chandrakant Shyam Tirodkar, Bhupesh Nilkant Padolkar, Ashwek Gawas, Pramesh Agnel Institute of Technology and Design Dept. Computer Engineering Mapusa India

ISBN: (纸本)9798350382952

Fruits are rich in nutrients, vitamins, and minerals and play a crucial role in both dietary health and a nation's economy. In India, diverse fruit cultivation necessitates efficient identification methods. The manual fruit identification processes are time-consuming and biased, leading to agricultural inefficiencies. Hence, the adoption of machine learning and deep learning has gained immense popularity in the automation of the fruit identification preprocess. This study evaluates the performance of DL models, Faster R-CNN and YOLO v8, on a dataset featuring five fruits namely Apple, Cashew Apple, Banana, Mango and Orange for identifying fruits with the comparison between them. While both models yield impressive results, Faster R-CNN generally exhibits higher mAP. However, YOLO v8 excels in precision for specific fruits like Cashew Apple and Banana. The choice between models involves a trade-off between mAP and precision, emphasizing the need for further optimization considering factors like dataset variations, computational performance, and real-world applicability. © 2024 IEEE.

关键词： Fruits

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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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