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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Anitha, Cuddapah Mahaveerakannan, R. Govindaraju, Sakthi Thendral, Tamil M. Sunkesula, Haseena Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Rajampet Andhra Pradesh Annamayya India

ISBN: (纸本)9798350374933

Many innovations in farming and manufacturing have helped the world's seven billion inhabitants get the food they need. Diseases and pests provide the greatest risk to food products because they reduce both the number besides quality of food. Various methodical methods have been devised to enhance food production while also reducing the risk of pests and illnesses affecting plants and fruits. The diagnosis of diseases is still dependent on human skill and manual labor when using these techniques. These past ten years have seen widespread usage of AI for the automation of various tasks, including harvesting in agriculture. Processing photos and identifying things using Machine Learning procedures is gaining popularity. We can automate harvesting by identifying plant diseases using Machine Learning algorithms;this will allow us to grow more food while cutting down on crop losses. Using the parallel convolution neural network (PCNN) suggested in this paper to detect peach disease traits-using the improved elu activation function rather than the traditional ReLu activation function-we first create an image denoising procedure to mitigate the impact of the complicated background on the images. Next, the classification accuracy is enhanced by fine-tuning the hyper-parameters of PCNN using the Archerfish hunting optimizer (AFHOA) model. This method proved effective for peach disease diagnosis, as evidenced by the results of 25513 photos. The greatest detection accuracy for black spot, peach was almost 92% to 96% accordingly. © 2024 IEEE.

关键词： Fruits

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Cloud-Based AI Solutions for Personal Finance Management

Cloud-Based AI Solutions for Personal Finance Management

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chhabra, Gurpreet Kaur Mishra, Ashutosh Kumar Tyagi, Varsha Mani, Chandra Tyagi, Vani Sagar, Pramod Kumar Management Education and Research Institute Department of Management Delhi India Srm Business School Department of Management UP Lucknow India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Mangalmay Institute of Engineering & Technology Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

Cloud-based AI answers for private finance control are out-of-the-field tools that leverage synthetic intelligence and cloud computing technologies to manage people's money higher. In this article, I have described various solutions with features ranging from budgeting to personalized financial planning and investment suggestions. Utilizing superior system learning algorithms and cloud-based AI solutions can help understand an individual's fiscal information, including expenses, profits, or savings transactions, for generating personalized insights and advice. This lets people learn about their spending habits and make more informed financial choices. Through the power of the cloud, these solutions can securely store and access financial data from multiple devices, which helps individuals, manage their finances on the go easily. It also enables the client to get real-time updates and notifications, which offers him more comprehensive knowledge regarding his existing financial scenario. These offerings also come with more automation features like reminders to pay payments and computerized enough transfers for a price range, keeping customers from spending further effort and time on their funds. They also offer financial advisors, tax planning tools, and wealth management. © 2025 IEEE.

关键词： Decentralized finance

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in computing research on science engineering and Technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 83-109页

作者： Anbazhagu, U.V. Koti, Manjula Sanjay Muthukumaran, V. Geetha, V. Munrathnam, Meram Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of MCA Dayananda Sagar Academy of Technology and Management Bangalore560082 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Campus 603 203 India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.k.Valley Idupulapaya Andhra Pradesh Kadapa516330 India

The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact. © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Decision making

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Challenges in Recycling Lead Acid Battery and Lithium-Ion Battery: A Comprehensive Review

Challenges in Recycling Lead Acid Battery and Lithium-Ion Ba...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Anuradha, S. Manimegalai, R. Devasena, M. PSG Institute of Technology and Applied Research Department of Computer Science Coimbatore India Indian Institute of Technology Global Sanitation Centre of Excellence Palakkad India

ISBN: (纸本)9798350378177

Battery recycling has become essential due to the rising demand for batteries and the associated risk to the environment caused by the disposal of batteries in various industries. It has become a global concern to recover significant minerals and metals from discarded batteries. The rapid expansion of the electric vehicle market causes a significant challenge in managing battery waste where Lithium-ion batteries (LIBs) are used. Lithium-ion batteries are the best alternative to energy storage devices, which encourages the development of electric vehicles, which in turn reduces greenhouse gas emissions (GHG) pollution in the transportation sector. 28 % of greenhouse gas emissions and 24% of the world's energy consumption are due to the transportation industry globally. The increasing usage of LIBs can result in the accumulation of spent LIBs. To prevent improper disposal of battery waste, recycling approaches can be adopted, which can in turn help reduce landfill waste and enhance environmental quality. This study compares the difficulties of recycling Lead Acid Battery (LAB) and Lithium-Ion Battery (LIB) wastes, emphasizing the need to implement efficient battery recycling procedures towards a circular economy. © 2024 IEEE.

关键词： Land fill

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain 5

Secure Goods Storage and Anti-Theft Approach using Ethereum ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Amasala, Likhitha Ponnuru, Mahesh Srideviponmalar, P. School of Computer Science and Engineering Vellore Institute of Technology Amaravathi522237 India School of Computing SRM Institute of Science and Technology Potheri603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart contract

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Fake News Detection Approach Based on Logistic Regression in Machine Learning 7th

Fake News Detection Approach Based on Logistic Regression in...

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7th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2022

作者： Sudhakar, M. Kaliyamurthie, K.P. Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

ISBN: (纸本)9789811993039

Today, social media is allowing everyone to spread information without any cost, and only a few people will do the small investigation and fewer filters than before. Political events are one of the increases in the popularity and spread of fake news. The increasing of fake news is the major problem of today and will negatively impact the world. To avoid this type of bogus information spreading worldwide, we can use artificial intelligence and machine learning. Recently, deep learning techniques used complex natural language processing also to providing the solution for counterfeit news detection. Facebook is using AI techniques to filter the fake news stories from the user feeds. Now it is possible using the Naïve Bayes classifier to differentiate between fake news and real news. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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Retroactive data structure for protein–protein interaction in lung cancer using Dijkstra algorithm

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1239-1251页

作者： Rangarajan, Prasanna Kumar Gurusamy, Bharathi Mohan Rajasekar, Elakkiya Ippatapu Venkata, Srisurya Chereddy, Spandana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai 601103 India Birla Institute of Technology and Science Pilani Dubai Campus Dubai United Arab Emirates

Protein–Protein interactions (PPI) networks are protein complexes arranged in networks that are created by biochemical processes or electrostatic forces to carry out biological functions. PPI research is a challenging problem for both computer scientists as well as biologists due to the inherent potential of the original source that researchers can work with. PPI networks are being employed to research ailments, develop better medications, and comprehend various medical and health procedures that will finally benefit mankind. Retroactive data structures are a type of data structure that allows actions to be done on it in the past. The only restrictions included in the data structure are consistency requirements, which allow for arbitrary operation insertion and deletion at arbitrary moments. It is shown that efficient retroactivity, unlike persistence, is not always possible. In this work, the performance demonstrated their utility through real-world use cases and applications, in addition to going through the data structures employed in their implementation. The dynamic single source shortest path problem was successfully resolved by modifying the Dijkstra algorithm dynamically. The Dijkstra method is changed into a dynamic one via the retroactive priority queue data structure. Retroactive data structures identify the group of updated vertices one by one, requiring fewer calculations to accept the modifications. In order to dynamically adjust the Dijkstra method, which solves the dynamic single source shortest path problem with a quick update time, we used a suitable dynamic graph representation and a retroactive priority queue. A protein-protein interaction network is created for the protein involved in lung cancer and then the modified Dijkstra algorithm is applied to calculate the all-pair shortest path. Finally based on different metrics top 10 genes were summarised. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Ap

关键词： Dynamic Dijkstra algorithm Persistence Protein–protein interactions Retroactive data structures Retroactive priority queue Shortest path problem

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Joint Communication and Synchronization Performance Optimization in Digital Twin Enabled Networks

Joint Communication and Synchronization Performance Optimiza...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Hanzhi Liu, Yuchen Chen, Mingzhe University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral GablesFL33146 United States North Carolina State University Department of Computer Science RaleighNC27695 United States

ISBN: (纸本)9798350351255

In this paper, we investigate an accurate synchronization between a physical network and its digital network twin (DNT) that is a virtual representation of the physical network. The considered network includes a physical network where a base station (BS) serves a set of users, and a DNT that evolves with the status of both DNT and the physical network. The BS must use its limited spectrum resources to serve the users, as well as transmit the physical network information to the cloud server for DNT synchronization. Since the DNT can predict the physical network status, the BS may not need to transmit physical network information to the server at each time slot thus saving spectrum resources to serve users. However, if the BS does not transmit physical information to the DNT over a long period of time, the DNT may not be able to represent the physical network accurately. To this end, the BS must determine whether to send physical network information to the server to update DNT and the spectrum resources used for physical network information transmission and serving users. We formulate this resources allocation problem as an optimization problem aiming to maximize the sum of data rates of all users, while minimizing the gap between the states of the physical network and the DNT. The formulated problem is challenging to solve by conventional optimization methods, since the BS may not be able to know the future status of the DNT. To solve this problem, we design a gate recurrent unit (GRU) and soft action-critic (SAC) based algorithm. The GRU enables the DNT to predict its future states by using historical state data, and updating the DNT when the BS does not transmit physical network information. The SAC based algorithm enables the BS to learn the relationship between the physical network information transmission and the future status estimation accuracy of the DNT thus determining whether to transmit physical network information to the cloud server, ensuring an accurac

关键词： Resource allocation

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