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2023 3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

2023 3rd International Conference on Advances in Electrical,...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

ISBN: (纸本)9781665494007

The proceedings contain 204 papers. The topics discussed include: a Bengali word identification and verification using machine learning approach;a framework for big data adoption and sustainable institutional performance;conceptual design of an automated irrigation and fixation system for long-term crop care of cherry tomatoes for applications in microgravity machines;comparative study of deep learning models for COVID 19 diagnosis;improved double-level blending model (IDLBM): customer churn estimating in insurance industry;a comparative survey of maize leaf diseases using pre-trained convolutional neural networks;chromatic partitioning of a network and an optimum scheduling problem;comparison of performance of machine learning algorithms for diabetes detection;fine - grained sentiment analysis on online reviews;an approach to predict and classify retinopathy stages in diabetic patients using deep learning;intelligent recognition of ancient Brahmi characters using transfer learning;and comparative study and performance analysis of deep neural networks for sign language recognition using transfer learning.

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Moving toward an intelligent edge: Machine learning and Wireless Communication 3

Moving toward an intelligent edge: Machine Learning and Wire...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Patil, Priyanka Pravin Perez-Mendoza, Adolfo Joshi, Kapil Shah, Heli Pillai, Biju G. Kalyan Chakravarthi, M. Dyiemr Akurdi Pune India Universidad Tecnológica Del Peru Uttaranchal University Uttaranchal Institute of Technology India Parul University Pit India Sri Balaji University Faculty of Management Department of It & Business Analytics Pune India VIT-AP University School of Electronics Engineering Amaravathi522237 India

ISBN: (纸本)9798350399264

In order to improve performance, efficiency, and yield, the intelligent wireless system concentrates on collaborating with cutting-edge techniques such as machine learning (ML) and similar methodologies. The research includes evaluating precisely the important factors that ML methodologies use to assist more intelligent network communication in many sectors. For the research, the researchers tried to collect both primary and secondary data. In order to have greater input and perspectives into the research field, the participants are recruited from the business. The main aspects taken into consideration for the research are spectrum availability and control, important congestion point detection, ML-influenced hotspot management, and control of hotspots. According to the research, which was conducted using the SPSS data analysis tool, it can be concluded that all of the variables have a significant impact on intelligent communication. As a result, ML contributes significantly to improving the user experience. © 2023 IEEE.

关键词： Machine learning

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Women Safety Platform with Safe Route Prediction using Crime data 3

Women Safety Platform with Safe Route Prediction using Crime...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sandyal, Krupashankari S. Anisha Sejal, B.S. Gupta, Laxmi Raj Bansal, Akshiv Department of ISE Dayananda Sagar College of Engineering Karnataka Bangalore India

ISBN: (纸本)9798331527549

Women's safety in public spaces is a pressing concern, particularly in urban environments where traditional navigation systems like Google Maps fail to prioritize safety over efficiency. This paper proposes a Women Safety Platform that leverages geospatial data, machine learning models, and user feedback to address these limitations. The platform predicts and highlights the safest route between source and destination by analyzing historical crime data and assigning safety scores to streets using machine learning models, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, Decision Tree, and Bernoulli Naive Bayes. KNN effectively identifies patterns in crime data to provide accurate safety scores, while Random Forest and Decision Tree classifiers achieved perfect training accuracy, showcasing their ability to capture complex patterns, albeit with potential risks of overfitting. Logistic Regression demonstrated a balance of high accuracy and generalization, and Bernoulli Naive Bayes provided reliable performance with the least overfitting tendency. Routes are evaluated based on aggregated street safety scores, visually marked to aid user decision-making and complemented with sentiment-based insights from user feedback. The Women Safety Platform uses geolocation data to monitor user safety and triggers an emergency SOS if safety levels drop below a threshold. By integrating crime data, real-time geospatial APIs, and proactive emergency measures the Women Safety Platform empowers women with a navigation tool that prioritizes safety alongside convenience. This initiative not only fosters confidence and freedom in navigating public spaces but also serves as a scalable model for enhancing mobility and safety for other vulnerable populations. © 2025 IEEE.

关键词： Logistic regression

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An Identification of Guava Fruit Disease Using ML 3

An Identification of Guava Fruit Disease Using ML

引用

3rd international conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Ahmed, Khandakar Rabbi Salam, Towfika Nandi, Subarna Hasan, Nazmul Rashid, Omor Faruk Al Miraz, Shafiun Reza, Md. Shafi Rahman, Farjana International University of Business Agriculture and Technology Uttara Model Town Miyan Research Institute Dhaka Bangladesh Kazla Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh London South Bank University Dept. of Data Science 103 Borough Rd LondonSE1 0AA United Kingdom Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jeonbuk National University Graduate School of Integrated Energy of AI North Jeolla Jeonju Korea Republic of

ISBN: (纸本)9798350388282

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers' socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used. © 2024 IEEE.

关键词： Fruits

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intelligent data Management in Cloud Using AI 3

Intelligent Data Management in Cloud Using AI

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Khan, Mohd Amaan Walia, Ranjan Chandigarh University Mohali Punjab Gharaun India

ISBN: (纸本)9798350381931

The rapid growth of data in various domains has necessitated the need for efficient and intelligent data management techniques in cloud storage. This research paper explores the application of AI-driven approaches to enhance data accessibility, security, and performance in cloud storage ***, the paper investigates the challenges faced in traditional data management systems, such as limited accessibility, potential security vulnerabilities, and performance bottlenecks. These challenges are addressed by leveraging the power of artificial intelligence *** research presents an overview of the role of AI in data management, including machine learning algorithms, natural language processing, and computer vision. These AI-driven approaches enable intelligent data classification, indexing, and retrieval, facilitating improved data accessibility in cloud storage ***, the paper discusses how AI can enhance security measures in cloud storage. It explores the use of AI-based anomaly detection algorithms to identify and mitigate potential security threats, such as unauthorized access, data breaches, and malicious activities. AI-driven encryption techniques are also explored to strengthen data protection in cloud *** addition to accessibility and security, the research paper delves into AI-driven techniques to optimize the performance of cloud storage systems. It investigates the application of machine learning algorithms for intelligent resource allocation and workload balancing, leading to enhanced system performance and reduced ***, this research paper highlights the significance of intelligent data management in cloud storage and showcases the potential of AI-driven approaches to address the challenges associated with data accessibility, security, and performance. The findings of this study can assist cloud service providers, researchers, and practitioners in developing more efficient and robust data management solu

关键词： Information management

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Optimization of Electrical Equipment in intelligent Transmission engineering Under Fuzzy Neural Network Algorithm 3

Optimization of Electrical Equipment in Intelligent Transmis...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Li, Xiping Wang, Jitao School Of Intelligent And Electrical Electronic Engineering Harbin Cambridge College Heilongjiang Harbin China School Of Electrical And Electronic Engineering Harbin Institute Of Technology Heilongjiang Harbin China

ISBN: (纸本)9798350318609

Traditional optimization analysis of electrical equipment in transmission engineering is often based on experience and rules, lacking intelligent methods. This leads to limitations in the adaptability and effectiveness of traditional methods when facing complex and variable power system conditions. By introducing the fuzzy neural network (FNN) algorithm, this paper fully utilizes its adaptability to nonlinear and complex correlations, thereby improving the intelligence level of optimization analysis of electrical equipment in transmission engineering. It collects actual power system operation data information and preprocesses the data to ensure the quality of the data. This article constructs a FNN model to effectively handle fuzzy and uncertain information, and applies it to the complex environment of power systems. The experimental results show that the average RMSE of the FNN for electrical equipment optimization prediction is 0.07, and the optimized voltage is very stable. The application of FNN algorithm can effectively improve the optimization effect of electrical equipment. © 2024 IEEE.

关键词： Fuzzy neural networks

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Research on Collaborative Optimization of Big data Analysis and Artificial Intelligence Based on Deep learning 24

Research on Collaborative Optimization of Big Data Analysis ...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhang, Xu Li, Suruo Chen, Huafeng Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China Jingchu Univ Technol Big Data Res Ctr Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Drawing upon deep learning techniques, this study investigates big data analysis methods across various scenarios to enhance data value. The proposed intelligent collection device is capable of harvesting both unstructured data, such as videos and images, and structured data, including geographical coordinates, operational metrics, and electronic device identifiers. Building on this, the paper introduces an architecture for a big data analysis system underpinned by deep learning, elaborating on its critical technologies. Furthermore, it underscores how community and end-to-end power transactions bolster the adaptability and maximize the value of power generation entities. Although Multi-Agent Deep Reinforcement learning offers a novel approach to managing energy among multiple prosumers, challenges such as environmental volatility, the safeguarding of prosumer privacy, and computational demands persist. This research aims to explore a multi-agent reinforcement learning algorithm that employs parameter sharing and deep deterministic policy gradients to enhance learning efficiency and mitigate training complexities through shared strategies and experiences among agents. Additionally, by leveraging a reputable third party for disseminating comprehensive community market data to prosumers, this approach not only promises to secure prosumer privacy effectively but also to minimize environmental uncertainty and augment the algorithm's scalability.

关键词： Deep learning Big data analysis Artificial intelligence collaborative optimization

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An Edge Computing Framework for data Analysis in intelligent Transportation Systems 3

An Edge Computing Framework for Data Analysis in Intelligent...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Sharma, Sumit Bali, Rasmeet Singh Chandigarh University Computer Science Engineering Punjab India

ISBN: (纸本)9798350381931

intelligent Transportation Systems (ITS) have been identified as a key application of the Internet of Things (IoT). In order to reduce latency and bandwidth needs, edge computing offers a distributed computing architecture that can process and analyse data closer to the source. In this paper, we propose an edge computing framework for data analysis in ITS and present experimental results to evaluate its performance. The proposed framework aims to address the challenges associated with traditional data analysis techniques by leveraging the power of distributed computing resources, advanced analytics, and real-time processing capabilities. The rationale behind the framework's design and architecture is to improve the efficiency, scalability, and adaptability of ITS solutions, ensuring faster decision-making, reduced latency, and enhanced overall transportation performance. © 2024 IEEE.

关键词： Edge computing

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Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm with Optimized Parameters: Overcoming LSTM Ambiguities for Improved Accuracy and Efficiency 3

Enhanced Bitcoin Price Prediction using RNN-GRU Algorithm wi...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kathiravan, M. Meenakshi, M. Kaviya, M. Sreesubha, S. Sathyadurga, V. Neela Gandhi, V. Chennai India Department of CSE Department of Computational Intelligence SRM Institute of Science and Technology Kattangulathur Chennai India Department of Artificial Intelligence And Data Science Panimalar Engineering College Chennai India Department of AIML Rajalakshmi Engineering College Thandalam Chennai India Department of CSE Hindustan Institute of Technology and Science padur Chennai India Department of CSE Panimalar Engineering college Chennai India

ISBN: (纸本)9798331527549

Bitcoin's decentralised character and introduction in January 2009 can be mostly attributed to Satoshi Nakamoto, the unidentified designer of the digital money. Unlike regular money, Bitcoin has no value in and of itself and is not supported by any government or financial institution. The "mining" process consumes a lot of resources thus it cannot manage trades without a network of computers, often known as "nodes" or "miners." The Bitcoin blockchain, a catalogue of all the past transactions, is maintained by these nodes. Like equities, bitcoin is getting more and more well-known even if it is somewhat erratic as an investment. Its value varies greatly;hence it is difficult to predict when prices will adjust. Since Bitcoin is so erratic, automated approaches are growing more crucial for future projections regarding it. Often utilised for prediction, long short-term memory (LSTM) networks have constraints. LSTM focusses on short-term input and has a complex and occasionally ambiguous design, thus some claim it does not operate well when prior data is not significant to current forecasts. This has caused some to wonder if one can forecast Bitcoin's price using this approach. Made as a substitute for standard RNNs, the Gated Recurrent Unit (GRU) helps to prevent the fading gradient issue. Bitcoin price projections made by the GRU design are far more accurate than those derived from more antiquated methods. © 2025 IEEE.

关键词： Long short-term memory

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A Deep-learning Approach for the Identification of New Subtypes of Lung Cancer 25th

A Deep-Learning Approach for the Identification of New Subty...

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25th international conference on intelligent data engineering and automated learning

作者： Banerjee, Tuhin Corradini, Andrea Canon EMEA Amstelveen Netherlands MCI Innsbruck Austria

ISBN: (纸本)9783031777301;9783031777318

Lung cancer is the deadliest cancer in the world. It is caused by unchecked cell division of damaged cells in the lungs forming tumors that eventually prevent the lung from functioning properly. Identification of novel unsupervised subtypes of lung cancer is critical to reveal new insights into the underlying biology of cancer and ensure that patients receive specialized precision treatment based on the subtype of cancer they are suffering from. The ability of modern sequencing tools to produce patient-specific RNA sequencing (RNA-seq) gene expression data has transformed cancer research by offering in-depth understanding of the molecular landscape of cancer. This paper reports on a pipeline that comprise of a Deep Autoencoder (DAE) model coupled with hierarchical agglomerative clustering (H-Clust). It aims to identify new unsupervised lung cancer subtypes from RNA-seq expression samples collected from a publicly available dataset. Further, a deep learning (DL) model, Artificial Neural Network (ANN) is used to classify a patient's data into one of the newly identified subtypes.

关键词： lung cancer subtype TCGA RNA-seq deep learning DAE encoder-decoder agglomerative clustering ANN

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