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

作者： Alkhayyat, Ahmed Kumar, Raman Al-Jawahry, Hassan M. Pareek, Bhagwati Lal Kaur, Jaspreet Jagtap, Jayant College of Technical Engineering The Islamic University Najaf Iraq School of Mechanical Engineering Rayat Bahra University Mohali India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Sangam University Rajasthan Bhilwara India Chandigarh Engineering College Chandigarh Group of Colleges Department of Computer Science Engineering Jhanjeri Punjab Mohali India Nims School of Computing Science and Artificial Intelligence Nims University Rajasthan Jaipur India

ISBN: (纸本)9798350387490

The current study focuses on the application of Intelligent Systems in the improvement of data analysis for climate changevariables using remote sensing systems. The idea is to enhance greatly interpretability of sophisticated remote sensing data, which is essential for analyzing the effects of the climate change. The use of the AI means that the climate ascertaining process of big data patterns can be expedited and consequently augment the knowledge in climate science. This research will further explore on the approaches and uses of these technologies so as to demonstrate how they can be used to enhance knowledge on climate change. the study will use advanced models from the AI classification, including CNNs and RNNs. They were used where the climate work is most sensitive: for land cover classification, vegetation health assessment, and the identification of climatic abuses such as glaciers' melting and heat island effect. CNN models gave much better accuracy in classification with accuracies as high as 92. 7 percent for actual experiment results compared to conventionally developed methods. The research also managed to assess the vegetation health using the NDVI analysis and identify severe climatic fluctuations. They stress the positive impact of the key ideas of artificial intelligence on enhancing the accuracy and frequency of the identification of environmental changes for improving climate change mitigation and adaptation. Future work will concern the further development of the AI models, the comprehensive integration of multi-source data as well as the further improvement of the models explainability to increase the AI solution's contribution to the actual scientific analysis of the global climate. © 2024 IEEE.

关键词： Convolutional neural networks

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems

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Computer Modeling in engineering & sciences 2022年第4期131卷 23-26页

作者： Qi Liu Xiaodong Liu Radu Grosu Ching-Nung Yang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburgh018373UK Institute of Computer Engineering Vienna University of TechnologyWien1040Austria Department of Computer Science and Information Engineering National Dong Hwa UniversityHualien97401Taiwan

Cyber Physical Systems(CPS)have been appealing in recent years as a result of the rapid emergence of unique hardware and software compositions that create smart,autonomously behaving ***-to-end procedures and new types of user-machine interaction are made possible via the *** the one hand,these CPS applications have the potential to deliver crucial services in a variety of developing application areas,including energy management,healthcare,traffic monitoring,industrial assessment and surveillance.

关键词： CPS hardware services

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Synergistic Skin Cancer Classification: Vision Transformer alongside MobileNetV2 4

Synergistic Skin Cancer Classification: Vision Transformer a...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Kumar, M Ranjith Priyanga, S. Anusha, Janani Srinivasan Chatiyode, Veda Santiago, Jerome Revathi, P. Vellore Institute of Technology School of Computer Science and Engineering Chennai India Amrita School of Computing Dept. of Computer Science and Engg. Amrita Vishwa Vidyapeetham Chennai India C. Abdul Hakeem College of Engg. and Tech. Dept. of Computer Science and Engg. Melvisharam Vellore India

ISBN: (纸本)9798350349900

Skin cancer is one of the most common types of cancer in the world, and it poses major health risks due to its ability to spread quickly and metastasize. Early and accurate identification is crucial for treatment success and improved patient outcomes. This proposed work combines the MobileNetV2 and Vision Transformer (ViT) architectures to create a hybrid automated skin cancer classification technique. This technique aims to increase the accuracy and efficiency of dermatological diagnosis tools by combining MobileNetV2's effective feature extraction capabilities with ViT's self-attention mechanism. After testing on the HAM10000 dataset, this hybrid model outperformed individual models with a remarkable 96.3% classification accuracy. Not only did the integration of ViT and MobileNetV2 improve the classification accuracy but it also demonstrated how various deep-learning architectures work together to tackle complex image analysis tasks. Classifying skin cancers is critical to the medical industry because it allows for the early diagnosis of various dermatological disorders, allowing prompt intervention and treatment. These benefits can eventually improve patient outcomes and save lives in huge numbers. The findings of this research highlight the potential of deep learning to transform dermatological diagnostics and open the door to creating systems that will significantly impact clinical practice by detecting skin cancer with greater accuracy and efficiency. © 2024 IEEE.

关键词： Lung cancer

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Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought 38

Unlocking the Capabilities of Thought: A Reasoning Boundary ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qiguang Qin, Libo Wang, Jiaqi Zhou, Jinxuan Che, Wanxiang Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China School of Computer Science and Engineering Central South University China The Chinese University of Hong Kong Hong Kong

Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) on complex reasoning tasks. Recently, a series of studies attempt to explain the mechanisms underlying CoT, aiming to deepen the understanding of its efficacy. Nevertheless, the existing research faces two major challenges: (1) a lack of quantitative metrics to assess CoT capabilities and (2) a dearth of guidance on optimizing CoT performance. Motivated by this, in this work, we introduce a novel reasoning boundary framework (RBF) to address these challenges. To solve the lack of quantification, we first define a reasoning boundary (RB) to quantify the upper-bound of CoT and establish a combination law for RB, enabling a practical quantitative approach applicable to various real-world CoT tasks. To address the lack of optimization, we propose three categories of RBs. We further optimize these categories with combination laws focused on RB promotion and reasoning path optimization for CoT improvement. Through extensive experiments on 27 models and 5 tasks, the study validates the existence and rationality of the proposed framework. Furthermore, it explains the effectiveness of 10 CoT strategies and guides optimization from two perspectives. We hope this work can provide a comprehensive understanding of the boundaries and optimization strategies for reasoning in LLMs. Our code and data are available at https://***/LightChen233/reasoning-boundary. © 2024 Neural information processing systems foundation. All rights reserved.

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A Novel Methodology based Soil characteristic Analysis using Machine Learning Techniques 2

A Novel Methodology based Soil characteristic Analysis using...

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2nd International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2022

作者： Sood, Kiran Shamsher, Salim Thirumalaisamy, Manikandan Tyagi, Pallavi Suvarna, Nakirekanti Chitkara University Chitkara Business School India Navi Mumbai India Galgotias University School of Computing Science and Engineering India Amity University Amity College of Commerce and Finance Noida India Galgotias University School of Computing Science and Engineering U.P. Noida India

ISBN: (纸本)9781665437899

Every area of knowledge has been digitized since the advent of the computer, allowing individuals who have access to computer resources to access the information available. As a result, data is increasing at an exponential rate and in an enormous quantity in every area. Researchers have a particular interest in the agricultural sector, which is one such topic. We proposed three new classification methods to overcome these limitations: fast k-Nearest Neighbor, which generates training set prototypes using either the Elbow method or the Silhouette method, training set reduction k-Nearest Neighbor, which reduces training set using prototype selection, and hybrid k-Nearest Neighbor classification methods, which combine both prototype generation and prototype selection mechanisms to generate a prototype from an original training set The goal of all of these approaches is to cut down on the time and space needed for the classification job performed by the k-Nearest Neighbor classifier to be completed. We have tested our novel classification methods using a soil health card agricultural data set, and our results show that these approaches are successful at solving the issues we have identified. © 2022 IEEE.

关键词： Classification (of information)

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Matrix-Based Ant Colony System for Traveling Salesman Problem

Matrix-Based Ant Colony System for Traveling Salesman Proble...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Xu Li, Jian-Yu Chen, Chun-Hua Zhan, Zhi-Hui Kwong, Sam Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Software Engineering South China University of Technology Guangzhou510006 China Lingnan University Department of Computing and Decision Science Hong Kong College of Artificial Intelligence Nankai University Tianjin300350 China Hanyang University Ansan15588 Korea Republic of

ISBN: (纸本)9781665410205

Ant colony system algorithm (ACS), as an important evolutionary computation (EC) algorithm, has demonstrated significant advantages in solving complex optimization problems. However, traditional EC algorithms and traditional ACS algorithm often face the challenge of slow computational speed when dealing with large-scale problems. In recent years, matrix-based EC approaches have been proposed to accelerate the computational speed, which has obtained promising results in dealing with large-scale problems. However, most existing matrix-based EC algorithms are designed for continuous optimization problems, while the matrix-based approach integrated with ACS has not attracted enough attention, which will be efficient for solving large-scale discrete optimization problems. Therefore, in this paper, we propose a matrix-based ACS (MACS) algorithm and apply it to solve the traveling salesman problem (TSP). MACS is an innovative improvement over the traditional ACS algorithm, utilizing matrix operations to parallelly let ants select city and update pheromone. Experimental results show that the MACS algorithm has significantly better efficiency in accelerating computational speed while maintaining the remarkable problem-solving ability in solving large-scale TSP. © 2024 IEEE.

关键词： Ant colony optimization

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Explainable AI for Intrusion Prevention: A Review of Techniques and Applications 7th

Explainable AI for Intrusion Prevention: A Review of Techniq...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Chandre, Pankaj R. Vanarote, Viresh Patil, Rajkumar Mahalle, Parikshit N. Shinde, Gitanjali R. Nimbalkar, Madhukar Barot, Janki Department of Computer Science and Engineering MIT School of Computing MIT ADT University Loni Kalbhor India Department of Information Technology MIT School of Computing MIT ADT University Loni Kalbhor India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Kondhawa India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhawa India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

Intrusion prevention has become a critical concern in today's increasingly complex and interconnected digital systems. Traditional intrusion prevention methods often rely on rule-based approaches that can be inflexible and prone to false positives and false negatives. The emergence of explainable artificial intelligence (AI) techniques has the potential to provide more accurate and interpretable solutions for intrusion prevention. This review paper surveys the current state of the art in explainable AI techniques for intrusion prevention, including their strengths and limitations. We begin by providing an overview of intrusion prevention and the challenges faced by traditional methods. We then explore the different types of explainable AI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Integrated Gradients, and their application to intrusion prevention. We also discuss the different types of datasets and evaluation metrics used in explainable AI-based intrusion prevention, including public datasets such as NSL-KDD and UNSW-NB15. Finally, we highlight the future research directions in explainable AI-based intrusion prevention, including the need for larger and more diverse datasets, better evaluation metrics, and more comprehensive explainable AI models. Overall, this review paper provides a comprehensive overview of the state of the art in explainable AI for intrusion prevention, and serves as a valuable resource for researchers and practitioners in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Additives

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Botanic Alchemy: Cultivating Insight through Extensive Plant Disease Dataset and Reweighted Visual Analysis 2

Botanic Alchemy: Cultivating Insight through Extensive Plant...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Mustafa Nawaz, S.M. Maharajan, K. Anand, J. Vijay Malathi, G. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu India J.N.N Institutes of Engineeriung Department of Electronics and Communication Engineering Tamil Nadu India Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Diagnosing plant diseases research tackles the vital challenge of identifying plant diseases in agriculture by focusing on the intricate complexities found in images displaying diverse symptoms, scattered lesions, and complex backgrounds. To aid this endeavor, a substantial dataset of 220,592 images is assembled. The proposed method uses a reweighting strategy that targets both visual regions and loss. Initially, weights for segmented patches are calculated based on their discriminative potential obtained from cluster distributions. These weights are then seamlessly integrated into the training process, assigning them to individual patch-label pairs during weakly-supervised training, aiding in recognizing disease-specific regions. Features extracted from this trained network create a comprehensive feature representation. This integration incorporates the GRU algorithm to encode the sequence of weighted patch features. Thorough evaluations on the newly curated dataset and a public dataset demonstrate the effectiveness and superiority of this methodology. This research significantly advances the field of plant disease recognition in image processing, opening doors for further innovative exploration. © 2024 IEEE.

关键词： Learning algorithms

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Exploring Machine Learning Applications and Future Prospects in Drug Discovery 10

Exploring Machine Learning Applications and Future Prospects...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Prabhu, M. Ramkumar Nancy, P. Rosaline, R.Anto Arockia Pandian, A. Pasumpon Devipriya, A. Arunagiri, Bhuvaneswari Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Chengalpattu India Care College of Engineering Department of Computer Science and Engineering Trichy India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This study aims to provide a wide-ranging investigation of the ML-based drug discovery approach by discussing ML methods, results, and outcomes at the drug development process. The methodology covers the steps of data acquisition and preprocessing, feature engineering and selection, model selection and optimization, virtual screening of drugs, prediction of response to drugs, toxicity prediction, integration of multi-omics data, and validation and deployment of an ML model. The study shows superiority of ML-driven methods in terms of speeding up the candidate selection process, prediction of drug target relationships, toxicity prognosis, and integration of multiple molecular datasets for the discovery of new therapeutic targets and biomarkers. While the models might perform seriously in metrics and such, without the model interpretability, generalization, and ethics, there comes necessity to research more and to work together with various experts. A talk is given where a clear link between accurate delivering results, ethical management and regulations as well as a fully transparent reporting as can be done is given for responsible and fair ML technology development in drug discovery is made. If we cast our eye on the future, constant improvements and capital increases in ML based methods are set to achieve a fundamental breakthrough in drug development and individualized medicine and thus ultimately turn patients lives upside down for the better also global scale. © 2024 IEEE.

关键词： Forecasting

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