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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Dept. of Computational Sciences Brainware University Kolkata India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.

关键词： PSNR Digital images Instruments Automata Information processing Robustness Encryption S Box Resilience Random sequences

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Comparative Study of Cancer Classification by Analysis of RNA-seq Gene Expression Levels 13

Comparative Study of Cancer Classification by Analysis of RN...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Amruth, A. Ramanan, R. Paul, Rhea Jayan, Sarada Thakur, Amrita Nidhin Prabhakar, T.V. Amrita School of Computing Dept. of Computer Science and Engineering India Dept. of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Chemistry Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Cancer classification is in the spotlight of research in the medical domain;genetic inheritance plays a significant role in causing this condition. Certain similarities in DNA sequence can be prevalent in individuals who have cancer. The foremost intention of this paper is to put forth efficient cancer classification techniques that provide steady and substantial accuracy. An unconventional factor that is RNA-seq gene expression levels are considered here as opposed to familiar factors such as physical features or results of various imagery techniques. The classification is conducted by analyzing RNA-seq Gene Expression levels measured by a powerful sequencing system called the Illumina Hiseq platform. A comparative study of the application of various machine learning algorithms - Decision Tree, Random Forest, SVM, KNN, Naïve Bayes, Multinomial Regression is conducted in this paper. This method significantly reduces the computational complexity as against deep neural network approach which is used with various imagery techniques. Experimental observations suggest that SVM provides higher accuracy, cross-validation score, nearly ideal AUC-ROC curves, and better performance concerning the time required to fit the model and subsequently predict the cancer type. © 2022 IEEE.

关键词： Principal component analysis

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Predicting Customer Purchase Intent Using Logistic Regression: A Data-Driven Approach

Predicting Customer Purchase Intent Using Logistic Regressio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： C. Rajarajachozhan G. Karthika A. Rajakrishnammal M. Mano P. Nithish Kumar E. Jayahari Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu Dept. of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu Dept. of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development.

关键词： Logistic regression Adaptation models Accuracy Pipelines Companies Predictive models Real-time systems Data models Predictive analytics Business

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Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2023年第2期1卷 145-155页

作者： Donapati, Ramesh Reddy Cheruku, Ramalingaswamy Kodali, Prakash National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal506004 India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Dept. of Ece Hyderabad500090 India National Institute of Technology Warangal Department of Computer Science and Engineering Warangal506004 India

Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency. © 2023 IEEE.

关键词： Crops

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation 17

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Raha, Avi Deb Gain, Mrityunjoy Saha, Sudip Kumar Rahman, Md Shohanur Adhikary, Apurba Debnath, Rameswar Bairagi, Anupam Kumar Biswas, Sujit Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh KUET Dept. of Mechatronics Engineering Khulna Bangladesh Noakhali Science and Technology University Dept. of ICE Noakhali Bangladesh City University of London Computer Science Department EC1V 0HB United Kingdom

ISBN: (纸本)9798331531195

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes. © 2025 IEEE.

关键词： Distillation equipment

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Advanced Optimized Counter based Hierarchal Model to Predict Cancer's Disease from Cancer Patients Neurological Features

Advanced Optimized Counter based Hierarchal Model to Predict...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Laxminarayanamma, K. Krishnaiah, R.V. Sammulal, P. Institute of Aeronautical Engineering Dept. of Information Technology Hyderabad500043 India Electronics and Communication Engineering AP Guntur522212 India Jntuh Computer Science and Engineering Kondagattu Karimnagar Telangana Jagtial505501 India

ISBN: (纸本)9781665474511

Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal

关键词： Feature extraction

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A study on the E-commerce Trends using Data Analysis 3

A study on the E-commerce Trends using Data Analysis

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Peter Noronha, Joseph Terence Mahmood Khan, Shariq Sebastian, Stina Nabeel Mustafa, Syed Muhammad NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Dept. of Business Management Riyadh Saudi Arabia

ISBN: (纸本)9781665461412

Over the years, numerous businesses transitioned to creating online access, which helped them towards gaining access to a global presence. With the rise of ecommerce businesses accessed through websites or social media platforms, many seemed to have failed to generate sufficient revenue for themselves. Machine learning, artificial intelligence, and data science have aided retailers in managing their supplies, creating a brand for themselves with the review system, and providing the customer with recommendations. Moreover, with the help of these recommendations, customers have made the right choices when purchasing a product. Thus, this is what all established retailers and their customers demonstrate. Due to a massive increase in e-commerce businesses, it is significant to have details of all the ongoing strategies in the current business to ensure a reasonable growth rate. This study will interpret the different trends, platforms, and strategies involved in an ecommerce business. © 2022 IEEE.

关键词： Electronic commerce

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A Hybrid Distance Technique to Optimize the K-Nearest Neighbor Algorithm 2

A Hybrid Distance Technique to Optimize the K-Nearest Neighb...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Abdulmuttaleb Al-Musawi, Hussein Ali Aljaidi, Mohammad Sara, Massar Boostani, Reza School of Engineering Shiraz University Electrical and Computer Engineering Faculty Cse and It Dept. Shiraz Iran Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798350373363

Plenty of Bayesian-consistent, heuristic and statistical classifiers have been introduced. Although support vector machines, deep neural networks, and random forests exhibit acceptable performance in several fields, their primary challenge lies in the lengthy training phase, especially when dealing with big data. K-nearest neighbors (KNN) is one of the simplest, Bayesian consistent and most common algorithms for classification tasks which locally makes its decision and there is no need to any training phase, can be implemented by parallel processing algorithms which makes it suitable to deal with big datasets. The core of this classifier relies on measuring the distance between test samples and its neighbors. Therefore, designing a hybrid distance technique to better find relevant neighbors is of interest. In this research, a weighted combination five distances has been proposed to optimize the performance of the KNN algorithm on diverse 28 datasets. To select appropriate weights of distances for each dataset, particle swarm optimization algorithm was used due to its simplicity and high convergence speed. The proposed method was applied to the datasets, demonstrating acceptable performance compared to its rivals. © 2023 IEEE.

关键词： Classification (of information)

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User Authentication on Smart Speakers Leveraging Acoustic Imaging

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IEEE Transactions on Mobile Computing 2025年

作者： Ren, Yanzhi Xia, Zhiliang Li, Siyi Liu, Hongbo Yu, Jiadi Li, Shuai Li, Hongwei University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China Shanghai Jiao Tong University Dept. of Computer Science and Engineering Shanghai200240 China University of Oulu Finland

The user authentication has drawn increasingly attention as the smart speaker becomes more prevalent. For example, smart speakers that can verify who is sending voice commands can mitigate various types of attacks such as replay attack or impersonation attack. Existing user authentication solutions either cannot be applicable to smart speakers directly or require certain additional user-device interaction or pre-installed infrastructure, which may severely affect the user experience and create extra burdens to users. In this work, we propose a user authentication system utilizing acoustic images, which are derived from the smart speaker by emitting beep signals and sensing echoes from the user's body with its microphone array, as the proof for user authentication. Given the acoustic samplings of the reflected beep signal, our system designs a distance estimation component by applying a correlation based technique on the beamformed signal to estimate the distance between the user and microphone array. Our image construction component then constructs a virtual imaging plane using the estimated distance and steers the array towards each grid of the plane to generate an acoustic image of the user. We also propose a transfer learning-based method to derive efficient features from the constructed images, and employ SVM classifiers for accurate user authentication. Moreover, to ensure the accuracy of our system, our user direction estimation scheme could further estimate the DoA of the user's voice command to make sure that the user stands in front of the smart speaker during the authentication process. Our extensive experiments demonstrate that our system is robust and accurate across various scenarios. © 2002-2012 IEEE.

关键词： Acoustic imaging

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Evaluating Large Language Models for Automatic Register Transfer Logic Generation via High-Level Synthesis

arXiv

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arXiv 2024年

作者： Swaroopa, Sneha Mukherjee, Rijoy Debnath, Anushka Chakraborty, Rajat Subhra The Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India The Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

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