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Harnessing Blockchain and Deep Learning for Enhanced Energy Analytics in Photovoltaic Systems

Harnessing Blockchain and Deep Learning for Enhanced Energy ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Subbulakshmi, G. Sujatha, S. Saraswathi, V. Hemamlini, Ranganathan Rani St Joseph's College of Engineering and Technology Department of Artificial Intelligence and Data Science Engineering Chennai India Bit Campus Anna University Department of Computer Appication Tiruchirappalli India Chennai Institute of Technology Department of Computer Science Engineering Chennai India St. Peter's Institute of Higher Education and Research Department of Electrical and Elctronics Engineering Chennai India

ISBN: (纸本)9798331510022

The rapid evolution of solar energy in industrial sectors has sparked a growing interest in renewable energy, particularly within the context of smart grids and systems. The effective detection and management of energies within photovoltaic (PV) systems stand as crucial tasks in this landscape. This abstract introduces a proposed model aimed at exploring the integration of blockchain networks within large-scale solar energy infrastructures. The primary objective is to establish a framework that enables the distribution of solar energy in a decentralized manner to end-users while ensuring secure energy transactions between producers and consumers. Blockchain technology serves as the cornerstone of this model, encompassing intelligent techniques to address energy mitigation challenges within the solar energy network. By leveraging blockchain, the detection of energies within solar power plants becomes shared knowledge accessible to all users connected to the solar power network. The research within this framework emphasizes the exploration and application of diverse Deep Learning (DL) architectures, including autoencoders, Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Deep Stacking Networks (DSNs), and Deep Belief Networks (DBNs). Furthermore, this research extends beyond conventional machine learning approaches, incorporating knowledge-driven methodologies. It incorporates generative models such as adversarial networks and transfer learning, elevating the scope of machine learning in optimizing energy detection, distribution, and consumption within the block chain-based solar energy infrastructure. This interdisciplinary approach signifies a significant stride in fortifying renewable energy systems. By amalgamating blockchain's decentralized nature with sophisticated DL architectures and knowledge-driven models, it establishes a resilient and efficient renewable energy infrastructure capable of meeting escalating demands for sustainabl

关键词： Adversarial machine learning

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The Influence of Mobile Commerce on Consumer Behavior: A FCM-RF-DNN Analysis

The Influence of Mobile Commerce on Consumer Behavior: A FCM...

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2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Akram, Nawab Dahiya, Suman Chittapragada, Hemanand Periyasamy, V.M. Vidhya, P. Chauhan, Amit Magadh Professional Institute Patna India Universal Ai University Department of Management Karjat India Madanapalle Institute of Technology & Science Computer Science & Engineering Madanapalle India Sona College of Technology Department of Eee Salem India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Data Science Karur India Department of Life Sciences Bengaluru India

ISBN: (纸本)9798350360660

For m-commerce vendors, the difficulty is to decipher what factors impact customer actions in the ubiquitous mobile setting. In addition, companies are attempting to incorporate social media into their mobile approach in some way. This proposed approach to the findings of a qualitative exploratory study regarding the use of social media and smartphones within the framework of mobile commerce. Keep in mind the order of importance while doing data preprocessing, feature selection, and training the model. The usual steps in getting data ready for processing, such as cleaning it, identifying users and sessions, and finding episodes. The IS-DT suggested method's implementation technique is utilized in feature selection. Unified FCM-RF-DNN models need to be trained after features have been retrieved. Two state-of-the-art approaches, RF and DNN, are outperformed by the suggested approach. Following the implementation of the method, accuracy improved by 96.13%. © 2024 IEEE.

关键词： Mobile commerce

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An Efficient Shortest Path Optimized Algorithm for Vehicle Adhoc Networks System 7

An Efficient Shortest Path Optimized Algorithm for Vehicle A...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Shalini, K. Reddy, K. Harinath Punati, Kondalarao Kavin, K.S. Veena, P. Pushpalatha, K. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Shamshabad India Annamacharya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Rajampet India Hyderabad Institute of Technology and Management Faculty of Electronics and Communication Engineering Hyderabad500090 India Government College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Tirunelveli India N. S. S. Polytechnic College Department of Computer Engineering Pandalam Pathanamthitta India Coimbatore Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore641109 India

ISBN: (纸本)9798350372816

VANET (Vehicular ad-hoc network) is a network of advanced automobiles designed to solve the problem of mobility and, as a result, reduce the number of accidents. Because VANET is a type of MANET (mobile ad hoc network) surrounded by human-driven nodes, the social concept can be used to explain VANET routing decisions. This motivates academics to look into the possibilities of using Social Network Analysis (SNA) to create routing strategies. The most critical problem in any system is parcel drop. Social characteristics-based architectures encounter comparable challenges, resulting in inefficient message delivery. In this research, the dragonfly method is used to the shortest path methodology for fixed line VANET with the purpose of reducing package drop and increasing network reliability. This is made achievable by sculpted system of values centralization analysis, which improves the decision via dragonfly optimization methodology. The behavior of the using nodes capability then be determined by the importance computation. In this study, the sustainability of the optimized shortest path system is measured in terms of End to End delay, Energy Consumption, Packet Loss, and Throughput. The dragonfly algorithm's throughput evaluation is 650 kbps as compared to the traditional way. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Efficient Anomaly Identification and Segmentation Using Optimization Based Fuzzy Clustering Approach 3

Efficient Anomaly Identification and Segmentation Using Opti...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Rekha, P. Tilagul, Anand Badrinath, K. Ramaraj, Kottaimalai Thilagaraj, M. Prasath Alias Surendhar, S. Mvj College of Engineering Department of Information and Science Engineering Bengaluru India Sjc Institute of Technology Department of Information and Science Engineering Bengaluru India Sjc Institute of Technology Department Artificial Intelligence and Data Science Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Krishnankoil India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India Aarupadai Veedu Institute of Technology Department of Biomedical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350369083

A brain tumor represents an accumulation of atypical cells within our brain, which can either be malignant (cancerous) or benign (noncancerous). The method of distinguishing and categorizing these harmful tissues based on their types is known as brain tumor dissection. Magnetic Resonance Imaging (MRI), a prevalent imaging technique, is often employed to capture images of these tumors. However, manually segmenting the tumor from the MRI can be both time-consuming and susceptible to errors. Thus, a quick and accurate method for cerebral tumor delineation is desperately needed. Accurately delineating the sections of a tumor is the goal of segmentation, one of the most difficult tasks in the study of medical images. Deep learning methods have become an increasingly common choice for academics lately because of their outstanding precision and high efficacy for picture segmentation. The widespread use of Fuzzy c-means (FCM) clustering in image segmentation further underscores its effectiveness. Despite the FCM algorithm's impressive computational efficiency and widespread use, it falls short in one key area - it overlooks the spatial information of pixels. This oversight can lead to reduced noise robustness and less precise segmentation. However, this doesn't diminish its overall value;rather, it highlights an area for potential improvement. By incorporating the spatial and intensity data of pixels into Fuzzy C-Means (FCM), we've created an enhanced method known as Weighted FCM (WFCM) that significantly improves the clustering process. Additionally, we've developed a unique bio-inspired optimization algorithm - the artificial Hummingbird Algorithm (AHB). This algorithm mirrors the exceptional flight abilities and intelligent foraging strategies of hummingbirds in nature. The innovative combination of WFCM and AHB is proving to be highly effective in identifying brain tumors, providing superior segmentation results. This groundbreaking method is revolutionizing medical dia

关键词： Image segmentation

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Proposed Hybrid AI-Driven Framework for Stock Market Prediction

Proposed Hybrid AI-Driven Framework for Stock Market Predict...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Rajini S Shreya Vijay Mayuri G Moses John Wesley Jyothi Lakshmi N Department of Computer Science Engineering Kumaraguru College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

The Stock Market has developed as a revolutionary force, transforming global economies through democratizing wealth creation. With the use of artificial intelligence and Machine Learning, investors can navigate complicated financial terrain. While improving predictive accuracy, the underlying complexities and varied market opinions can render decision-making effective. This study emphasizes trend analysis and prediction of future values, enabling users to make smart investment decisions with little effort. Using a Hybrid Machine Learning Model, the method brings together several top-performing algorithms to improve predictive power and trend observation. The most important components of the project are Market data Retrieval, Time Series Forecasting, Sentiment Analysis and data Visualization, all of which assist in creating thorough reports that aid strategic investment decisions.

关键词： Accuracy Navigation Time series analysis data visualization Machine learning data retrieval Complexity theory Stock markets Forecasting Investment

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AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in Computer Vision for Autonomous Mobile Robots 23rd

AIMSM - A Mechanism to Optimize Systems with Multiple AI M...

引用

23rd EPIA Conference on artificial intelligence, EPIA 2024

作者： Ferreira, Bruno Georgevich Sousa, Armando Jorge Reis, Luis Paulo Augusto de Sousa, António Rodrigues, Rui Rossetti, Rosaldo Edge Innovation Center Federal University of Alagoas Maceió Brazil FEUP - Faculty of Engineering of the University of Porto Porto Portugal LIACC - Member of LASI LA - Artificial Intelligence and Computer Science Laboratory Porto Portugal INESC TEC - INESC Technology and Science Porto Portugal

ISBN: (纸本)9783031735028

This article proposes the artificial intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://***/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Detection of Pulp Stones in Radiographic Images: Survey Using artificial intelligence

Detection of Pulp Stones in Radiographic Images: Survey Usin...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Manasa, G.R. Venugopala, P.S. Santhosh, G. Umesh, G. NMAM Institute of Technology NITTE Deemed to Be University Computer Science and Engineering Nitte karkala India NMAM Institute of Technology NITTE Deemed to Be University Artificial Intelligence and Data Science Nitte Karkala India NMAM Institute of Technology NITTE Deemed to Be University Mechanical Engineering Nitte Karkala India Conservative Dentistry and Endodontics Bapuji Dental College and Hospital Davangere India

ISBN: (纸本)9798331517892

Pulp stones are calcifications within the dental pulp chamber that can complicate endodontic procedures and negatively impact dental health. Detecting pulp stones accurately is essential for effective diagnosis and treatment planning. Traditionally, manual inspection of radiographs by clinicians is employed for this task, but it is time-consuming and prone to variability. With advancements in artificial intelligence (AI), automated detection systems using deep learning techniques have emerged as promising alternatives. This literature survey reviews state-of-the-art AI-based methods for pulp stone detection, focusing on the application of machine learning and deep learning models. The review covers commonly used techniques, available datasets, evaluation metrics, and challenges faced in model development. A comprehensive analysis of the strengths and limitations of these approaches is provided, alongside a discussion on the potential for integrating AI into clinical practice. Future directions are suggested to address existing challenges, including the need for larger and more diverse datasets, improved model generalization, and better interpretability. This survey aims to guide researchers and practitioners in understanding the current landscape of AI applications in dental imaging, with a specific focus on the detection of pulp stones. © 2024 IEEE.

关键词： Deep learning

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Advanced Deep Learning Model for Enhanced Kidney Disease Detection 5

Advanced Deep Learning Model for Enhanced Kidney Disease Det...

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5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Davamani, K. Anita Dhanya, D. Pushparani, S. Vidhya, R. Mohan, Geetha Jani Anbarasi, L. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Agni College of Technology Department of Information Technology Chennai India Institute of Applied Technology Abu Dhabi United Arab Emirates Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Automated Chronic Kidney Disease (CKD) is a swiftly growing non-communicable condition that contributes to a substantial number of deaths and illnesses worldwide. Affecting over 10% of the global population, CKD results in millions of fatalities each year. In this study, an advanced deep learning model for kidney disease detection is proposed that combines convolutional and involution layers to improve classification accuracy. The model processes input images by first applying a 2D convolution with 32 filters of size 3x3, followed by ReLU activation to extract key features. Max-pooling layers are then used to reduce spatial dimensions while retaining crucial information. The convolutional layers are sequentially stacked with larger filter sizes, resulting in progressively richer feature representations. To enhance feature extraction, the model integrates custom involution layers, which extend traditional convolution operations and are particularly effective for processing large-scale image data. These involution layers are applied after the convolutional steps, utilizing 128 and 256 filters of size 3 × 3, followed by max-pooling to further refine the extracted features. A comparative analysis was carried out between the proposed custom Convolutional Neural Network (CNN) model and previous research works, utilizing metrics such as Recall, Precision, F1-Score, Accuracy, Loss, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves. The results indicate that the proposed model achieves superior classification prediction performance, with an accuracy of 99% and reduced computational complexity. Additionally, it demonstrates a enhanced F1-Score of 100%, 98%, 98% and 100% for Cyst, Normal, Stone and Tumor respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Cognitive Farming in Agriculture: Deploying CNN-RF for Maize Disease Precision Diagnostics 2

Cognitive Farming in Agriculture: Deploying CNN-RF for Maize...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Nisha Chandran, S. Garg, Ashish Namdev, Dheeraj Kukreja, Vinay Mehta, Shiva School of Computing Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India IIMT College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383652

A hybrid approach integrating both CNN and RF is investigated in this research study of the Survey regarding the classification of maize leaf diseases with the help of CNN and RF. Our research carefully contrasts five created disease severities as a reported diagnostic tool for agriculturalists. The high accuracy of the model was extensively validated by applying various performance metrics with excellent precision of the different classes ranging from 95.90% to 98.47% for recall ranged between 95.01% to 99.21%, and F1- scores ranged between 96.55% to 98.4. The model achieved a general accuracy of 97.42851 %. We employed various averaging approaches to generalize the performance results across varying illness levels. A macro average yielded an accuracy of 97.37%, recall of 97.48%, and F1-score of 97.42%, implying similarity of performance across classes. Considering class imbalance, the weighted average was accurate at 97.44%, recalled at 97.43%, and the F1-score was similar at 97.43%. The micro average, as the aggregation of results across classes, was remarkably stable at 97.43 %, representing the performance of the model well and especially favoring frequent courses. Early identification and classification of diseases affecting maize leaves significantly contributed to precision agriculture, and this paper presents a reliable and efficient method for a timely diagnosis. It shows the stable performance of the CNN-RF model because many statistical measures are indicated. This implies that it possesses reasonable practicality in agricultural applications that highly support pest control and increase crop yields. © 2024 IEEE.

关键词： Agriculture Convolutional neural networks (CNNs) Image classification Maize Leaf Diseases Random Forest

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Modified Term Frequency and Inverse Document Frequency with Optimized Deep Learning Algorithm based Fake News Detection 1

Modified Term Frequency and Inverse Document Frequency with ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Rana Veer Samara Sihman Bharattej, R. Prashanth, V. Alabdeli, Haideer Thottan, Sunaina Sangeet Ananthi, S. Saint Leo University Department of Computer Science FL United States Nitte Meenakshi Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Dhanalakshmi Srinivasan College of Engineering and Technology Department of Artificial Intelligence and Data Science Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331529246

The identification of real and fake news has become difficult task in the recent years due to more similar feature between them. As the issues of fake news are leading the miscommunication to the reads, various automated software has been introduced for the detection of fake news. The existing models tried to advance the identification of the fake new but failed to reach the expected outcomes due to overfitting issues and limited feature handling abilities. To overcome the existing issues the Latent Dirichlet Allocation based Term Frequency and Inverse Document Frequency with Brown-bear Optimization Algorithm-Bidirectional Long Short-Term Memory (LDA based TF-IDF with BOA-BiLSTM) is developed for the detection of fake news. The news data is collected and get cleaned by tokenization and normalization techniques. Then the LDA based TF-IDF model is used for the extraction of significant details of the news text data. Further the BOA-BiLSTM algorithm is used for the detection of fake news. The developed LDA based TF-IDF with BOA-BiLSTM algorithm has exhibited the better result having accuracy of 0.9489 and recall of 0.9012 for ISOT fake news dataset compared to the existing Knowledge Graphs for multi-domain fake news detection (KG-MFEND) model. © 2025 IEEE.

关键词： Deep learning

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