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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Afshari, Mahnaz Gandomani, Taghi Javdani Shahrekord University Data Science Research Group Shahrekord Iran Shahrekord University Dept. Computer Science Shahrekord Iran

ISBN: (纸本)9798350381580

Software effort estimation remains a persistent challenge and requires serious attention in the early stages of software project management. Inherent uncertainties arising from incomplete and inaccurate requirements pose a significant barrier to reliable estimations. Despite numerous efforts and various techniques proposed for cost estimation, the pursuit of improving estimation accuracy remains essential. In response to this challenge, this study introduces a novel model that integrates an Adaptive Neuro-Fuzzy Inference System with the Ant Colony Optimization algorithm. The model is further compared with well-known evolutionary algorithms such as Differential Evolution, Genetic Algorithm, Artificial Neural Network, and Particle Swarm Optimization. Applying the proposed model to popular software effort estimation datasets, including Albrecht, Desharnais, and Kemerer, demonstrates its superiority over the mentioned algorithms. The improved estimation provided by this model can assist software project managers in better project cost estimation. © 2024 IEEE.

关键词： Fuzzy neural networks

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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A Blockchain-Based Secure and Transparent Global Radioactive Source Lifecycle Management System

A Blockchain-Based Secure and Transparent Global Radioactive...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Poudel, Susan Kausar, Firdous Das, Debashis Chatterjee, Pushpita Hussain, Sajid Ghosh, Uttam Fisk University Dept. of Mathematics and Computer Science NashvilleTN United States Meharry Medical College Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798331504847

The management of radioactive sources is a critical process that ensures the safe and responsible handling of radioactive materials throughout their lifecycle. These sources require careful management from production to disposal, such as real-time tracking and radiation monitoring to follow everyone's safety rules and protect people and the environment. However, these sources present significant global challenges, especially regarding safety, security, and transparency. Current systems face limitations such as fragmented oversight, lack of accountability, and risks of unauthorized access. To address these limitations, this paper proposes a blockchain-based radioactive source lifecycle management system to manage the lifecycle of radioactive sources. Blockchain's decentralized and tamper-proof ledger secures data throughout the entire lifecycle of radioactive materials. By using smart contracts and access controls, the system ensures that only authorized parties can monitor and verify transactions in real-time, which reduces human error and prevents unauthorized changes to the data. Users can perform key operations such as retrieving source details, transferring ownership, updating source locations, and adding observers to our proposed system. Our experiment in designing and testing a blockchain application has proven the potential for a secure and transparent system that enhances global cooperation in managing radioactive sources. Overall, the proposed system not only addresses current challenges in radioactive source management but also enhances the tracking and monitoring of radioactive materials. © 2025 IEEE.

关键词： Radioactive wastes

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Quantum Inspired Zeroshot Classification For Adverse Drug Reactions Detection From Social Media Reviews

Quantum Inspired Zeroshot Classification For Adverse Drug Re...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Dey, Arijit Shrivastava, Jitendra Nath B. P. Poddar Institute of Management and Technology Dept. of Computer Applications WB Kolkata India Invertis University Dept. of Computer Science and Engineering UP Bareilly India

ISBN: (纸本)9798350379716

A deep learning model enhanced with quantum transformer has been introduced in detection of adverse reactions of drugs within the reviews collected from social media written in English language. This proposed model enhanced the human language understanding for detecting adverse reactions of drug. Quantum transformers are used for position embedding and attention purpose. Variational quantum circuit encoded the human language and identify the relevant components of the input reviews as class feature vectors to develop a relationship network for each drug correspond to the condition and review comments on which probability distribution functions applied to detect ADRs found in a medicinal product or not. The correctness of advanced model architecture built on quantum transformer is quite encouraging, and it performs better than other cutting-edge learning models. Achieved an accuracy of 93% with a loss of 0.0393 with this proposed model. Though it is too early to declare that quantum computing has completely replaced. This proposed quantum enhanced transformer helps to show the direction towards quantum enhanced hybrid model design in research. © 2024 IEEE.

关键词： Distribution functions

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A Survey on Fish Size Measurement in Aquatic Environments

A Survey on Fish Size Measurement in Aquatic Environments

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2024 IEEE Bangalore Humanitarian Technology Conference, B-HTC 2024

作者： Ankitha, K. Venugopala, P.S. Kumaki, Takeshi NMAMIT Dept. of Artificial Intelligence and Data Science Nitte India Ritsumeikan University Dept. of Electronic and Computer Engineering Kusatsu Shiga Japan

ISBN: (纸本)9798350344509

Fish size measurement in aquatic environments plays a pivotal role in understanding fish biology, ecology, and fisheries management. This survey explores various methodologies employed for fish size measurement, emphasizing their significance in aquaculture, fisheries, Ocean and ecological research. Techniques encompass both contact and non-contact methods, with advancements in imaging technologies allowing for non-invasive measurements. These approaches involve the use of cameras, image processing software, and advanced machinery to estimate fish size without causing stress or harm to the fish. While these methods offer promising avenues for precise measurements, challenges exist, including accuracy concerns and environmental limitations. Consequently, there's ongoing research to refine these techniques and ensure their applicability across different fish species and aquatic conditions. Overall, this survey highlights the evolving background of fish size measurement techniques and their importance in various domains within aquatic sciences. © 2024 IEEE.

关键词： Fish

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Smart Shopping Cart Using Radio Frequency Identification

Smart Shopping Cart Using Radio Frequency Identification

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Kesava, Gudipati Rishi Reddy, Vanukuri Karthik Trivikram, Sadineni Santhi Sri, K. Vignan's Foundation for Science Technology & Research Deemed to Be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350348460

The "Smart Shopping Cart Using Radio Frequency Identification"project revolutionizes the shopping experience through RFID technology, offering real-time cost updates to shoppers. This innovative system incorporates RFID readers, a microcontroller for instant cost computations, and an LCD screen for displaying the total expense. By tackling common issues associated with traditional shopping carts, like prolonged checkout procedures and the absence of live cost monitoring, our project significantly enhances the efficiency and convenience of shopping. Shoppers can now enjoy a seamless experience as the RFID technology effortlessly scans tagged products, allowing the microcontroller to promptly calculate the overall cost, which is then displayed on the LCD screen. This breakthrough not only streamlines the shopping process but also eliminates the need for manual price entries, reducing errors and enhancing overall customer satisfaction. The successful integration of these technologies marks a promising step towards the future of retail, where smart shopping carts pave the way for quicker, more accurate, and enjoyable shopping ventures. © 2024 IEEE.

关键词： Efficiency and Convenience Radio Frequency Identification (RFID) Real-time Cost Updates RFID Technology Integration Seamless Shopping Experience Smart Shopping Cart

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Stock Market Prediction Using Deep Learning

Stock Market Prediction Using Deep Learning

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Johnshaida, S.K. Kumar, G. Shiva Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350379525

This project focuses on leveraging One kind of deep learning model is the Long Short-Term Memory (LS TM) networks., to forecast stock prices by analyzing historical data and technical indicators. The methodology involves comprehensive steps such as data collection, preprocessing, feature selection, model selection (including LSTM networks), training, and evaluation taking advantage of performance indicators such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). It utilizes a dataset comprising historical gold stock prices and trading volume to train and test the LSTM model. Furthermore, Naive Bayes, Support Vector Machine (SVM), Linear Regression, and other machine learning algorithms are examined in the study, along with more complex methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Results reveal diverse accuracy levels across models, with LSTM demonstrating a tendency to overpredict stock prices. Future endeavors aim to refine model performance, investigate ensemble methods, and integrate additional data sources like economic news and sentiment analysis to bolster prediction accuracy, thereby offering investors and analysts enhanced decision-making tools in the dynamic stock market landscape. © 2024 IEEE.

关键词： Convolutional neural networks

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A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images 4

A Convolution Neural Network Based Classifier for Discerning...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Kumar, Uppala Eswar Basava Vivekreddy, Chintakuntla Sujatha, Kamepalli Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350354218

The exponential advancement of artificial intelligence (AI) has led to the creation of synthetic images that closely resemble real-world photographs. However, the widespread distribution of AI-generated content poses significant challenges in distinguishing between genuine and synthetic imagery, particularly in cybersecurity, content moderation, and image forensics. Traditional image classification methods struggle with this task due to the striking visual similarities between the two types of images. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, leveraging their ability to extract complex features from raw pixel data. In this study, we propose an enhanced CNN methodology to differentiate between AI-generated and real images. Specifically, we focus on three popular CNN architectures: ResNet-50, RexNet-150, and RexNet-200. By augmenting these models with advanced architectural enhancements and training strategies, we aim to improve their discriminative capabilities. Our experimental findings reveal that RexNet-150 surpasses ResNet-50 and RexNet-200, achieving superior accuracy in distinguishing between AI-generated and real images. RexNet-150 attained an impressive 97.9% accuracy on the test dataset, outperforming ResNet-50 (93.1%) and RexNet-200 (90.3%). Moreover, RexNet-150 demonstrated enhanced training convergence and speed, indicating its effectiveness in learning discriminative features for image classification tasks. The superior performance of RexNet-150 holds significant implications for online safety, content moderation, and image authenticity detection. These advancements in CNN technology foster trust in digital imagery and enhance confidence in AI applications across diverse domains. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Routing for IoT-Driven MANETs Using Computational Intelligence

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Procedia computer science 2025年 258卷 2798-2807页

作者： Ramanpreet Kaur Kavita Taneja Harmunish Taneja Dept. of Computer Science and Applications Panjab University Chandigarh India

The interlinkage of the Internet of Things (IoT) with Mobile Ad Hoc Networks (MANETs) creates an autonomous communication ecosystem, imperative for applications ranging from disaster recovery to smart agriculture. On the other hand, this coalescing gives rise to crucial challenges, especially in energy efficiency, higher transmission rates, and network connectivity. As the devices in IoT-driven MANETs have restricted battery life, the optimum use of energy is vital for extending network lifespan. The global explosion of IoT devices demands rapid data transfer and constant connectivity. Moreover, frequent topological changes brought on by the high mobility devices in IoT-driven MANETs elevate the chances of link failure. These disruptive actions undermine network productivity resulting in reduced throughput and higher latency. Computational intelligence approaches offer credible solutions for resolving these concerns. This study presents a Unified Energy-aware Link Auto-configuration Routing Model (UELARM) for IoT-driven MANETs that favors energy-aware paths and the auto-configuration of broken links. UELARM exploits the flower pollination algorithm by incorporating three vital parameters such as energy cost, average community time, and packet loss to explore the optimal route. Additionally, for automatically configuring broken links in IoT-driven MANETs, UELARM presents a route repair approach based on mobility prediction, drawing inspiration from the termite colony optimization algorithm. The simulation results indicate that the UELARM outpaces other state-of-the-art routing algorithms regarding energy consumption, throughput, delay and network lifetime.

关键词： Computational Intelligence Flower Pollination Link Repair MANET Routing Termite Colony

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