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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Prusty, Sashikanta Prusty, Sushree Gayatri Priyadarsini Sahoo, Ghanashyam Rautaray, Jyotirmayee Dalai, Sasanka Sekhar Padhy, Manjog Department of Computer Science & Engineering Bhubaneswar751030 India Gita Autonomous College Department of Computer Science & Engineering Odisha Bhubaneswar India Outr Department of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

In the recent two decades, cancer has been the leading cause of death around the globe. Breast Cancer (BC) is the second-highest, causing a maximum number of deaths globally these days. This, comprises pharmaceuticals, medical and surgical supplies, medical gadgets and equipment, and other products required to assist doctors, nurses, and other healthcare individuals. This work aims to address the requirement for BC specimen collection within healthcare organizations, as well as to predict BC before it happens. Firstly, we have carried out a methodology to determine the necessary steps for predicting BC. Secondly, breast screening and testing using X-ray techniques. Thirdly, a model called logistic regression (LR) to implement on breast mammograms to predict the BC disease. In the end, performance for this model has been measured by using precision, recall, f1score, and accuracy. Finally, it was found that the LR model achieves an accuracy of 97.36 % while detecting BC from breast mammograms. However, an ROC (Receiver Operator Characteristic) curve has been plotted, representing the false positive rate (FPR) and true positive rate (TPR) on the x and y-axis respectively. Thus, defining the LR model predicts the BC closer to '1', which might be a greater advantage for the doctors. © 2024 IEEE.

关键词： Forecasting

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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Hybrid deep learning-based intrusion detection system for wireless sensor network

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International Journal of Vehicle Information and Communication Systems 2024年第3期9卷 239-255页

作者： Gowdhaman, V. Dhanapal, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

Wireless Sensor Networks (WSNs) play an important role in the modern era and security has become an important research area. Intrusion Detection System (IDS) improve network security by monitoring the network state so that threats and attacks can be detected and rectified. With decades of development, IDS still lags in performance in terms of detection accuracy, false alarm rate and unknown attack detection. To overcome this performance issue, researchers implemented numerous machine learning techniques to detect the attacks. Conventional machine learning models identify the essential features through specific feature extraction techniques which increases the computation complexity of the system. For attack details and their sub-categories, deep learning technique is used in the proposed work. The detection model incorporates ResNet based on Inception with a support vector machine to detect WSN intrusions. Proposed algorithm is applied to Standard NSL-KDD data set and performance metrics like recall, precision, accuracy and f1-score are considered for analysis. The comparative analysis demonstrates the proposed model performance of 99.46% accuracy is better than traditional approaches like random forest, decision tree, deep neural network and convolutional neural network. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Deep neural networks

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Automated Synthesis of Distributed Code from Sequential Snippets Using Deep Learning

Automated Synthesis of Distributed Code from Sequential Snip...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Sanjel, Arun Khanal, Bikram Rivas, Pablo Speegle, Greg Baylor University School of Engineering and Computer Science Department of Computer Science United States

ISBN: (纸本)9798350362480

Processing big data poses a significant challenge when transitioning from sequential to distributed code, primarily due to the extensive scale at which data is handled. This complexity arises from both syntax and semantic differences between the codes. Unfortunately, current methods are inefficient and require more effective automated solutions. To address this problem, we utilized the Transformer-based BERT model because of its exceptional capability to understand and capture deep contextual relationships in large datasets. Our method involved creating two comprehensive datasets containing 10k and 100k sequential code snippets paired with their corresponding PySpark API calls. We optimized the BERT model by fine-tuning it to predict distributed API calls for previously unseen sequential snippets. For the 10k dataset, the model demonstrated robustness, achieving a training accuracy of 99.36%, a test accuracy of 99.7%, a Balanced Accuracy of 99.78%, and an F1 Score of 0.997. In contrast, the 100k dataset metrics were equally impressive, with a training accuracy of 89.48%, a test accuracy of 99.99%, a Balanced Accuracy of 99.98%, and an F1 Score of 0.999. Our work demonstrates the feasibility of automating the sequential-to-distributed code transition with notable precision. © 2024 IEEE.

关键词： Large datasets

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Transparency in Translation: A Deep Dive into Explainable AI Techniques for Bias Mitigation

Transparency in Translation: A Deep Dive into Explainable AI...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Deokar, Ruchira Nanjundan, Preethi Mohanty, Sachi Nandan Christ University Department of Data Science Pune Lavasa India School of Computer Science & Engineering VIT -AP University Andhra Pradesh India

ISBN: (纸本)9798350361537

In an era dominated by artificial intelligence (AI), concerns about bias and discrimination loom large. The quest for fairness and equity in AI-driven decision-making has led to the exploration of Explainable AI (XAI) as a viable solution. This paper undertakes a thorough examination of the bias ingrained within AI systems and posits XAI as a potent antidote. Beginning with an exploration of the origins and aftermath of bias in AI, the analysis traverses the evolution of XAI techniques, including SHAP, LIME, and counterfactual explanations, clearly stating their advantages and drawbacks. With each XAI method thoroughly inspected, the study unravels their applicability across diverse AI models and domains. Furthermore, a compelling case study is presented, showcasing XAI's practical application in a language translation app, where it guarantees transparency and equity in the translation process. This tangible example serves as a testament to XAI's efficacy in mitigating bias within real-world applications. As the analysis concludes, it underscores the pivotal role XAI plays in fostering accountability and trustworthiness in AI systems. By shedding light on how XAI mitigates bias and offering concrete examples of its utility, the paper advocates for its widespread adoption as an imperative step towards the development of ethically robust AI systems. In a landscape filled with concerns about bias, XAI emerges as a beacon of hope, promising a future where AI decisions are transparent, fair, and equitable for all. © 2024 IEEE.

关键词： Sensitivity analysis

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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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The Minimum Moving Spanning Tree Problem

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Journal of Graph Algorithms and Applications 2023年第1期27卷 1-18页

作者： Akitaya, Hugo A. Biniaz, Ahmad Bose, Prosenjit De Carufel, Jean-Lou Maheshwari, Anil da Silveira, Luís Fernando Schultz Xavier Smid, Michiel Department of Computer Science University of Massachusetts Lowell United States School of Computer Science University of Windsor Canada School of Computer Science Carleton University Canada School of Electrical Engineering and Computer Science University of Ottawa Canada

We investigate the problem of finding a spanning tree of a set of n moving points in Rdim that minimizes the maximum total weight (under any convex distance function) or the maximum bottleneck throughout the motion. The output is a single tree, i.e., it does not change combinatorially during the movement of the points. We call these trees a minimum moving spanning tree, and a minimum bottleneck moving spanning tree, respectively. We show that, although finding the minimum bottleneck moving spanning tree can be done in O(n2) time when dim is a constant, it is NP-hard to compute the minimum moving spanning tree even for dim = 2. We provide a simple O(n2)-time 2-approximation and a O(n log n)-time (2 + Ε)-approximation for the latter problem, for any constant dim and any constant Ε > 0. © 2023, Brown University. All rights reserved.

关键词： Trees (mathematics)

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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