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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Arthi, A. Aggarwal, Kapil Karthikeyan, R. Kayalvili, S. Srimathi, S. Srivastava, Aviral Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Computer Science & Engineering Guntur Andhra Pradesh Vaddeswaram522302 India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Artificial Intelligence College Name & Address: Kongu Engineering College Perundurai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India Amity University Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Android mobile applications and devices can be extremely utilized and employed in industry and smart cities. Malware recognition is an efficient and powerful method for assurance security of Android schemes, mainly for industrial platforms and smart cities. In recent times, research utilizing machine learning (ML)-based approaches for Android malware recognition enhanced quickly. However, one of the performed methods to accomplish feature selection and analysis, so-called feature engineering is labour-intensive and depends on artificial knowledge. Therefore, this study presents a Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification (HM3-AMRC) model. The proposed HM3-AMRC technique recognizes and categorizes the Android malware accurately and efficiently. In the presented HM3-AMRC technique, initial phase of data preprocessing is performed. Next, the classification process takes place using three ML techniques such as gradient boosting decision tree (GBDT), extreme learning machine (ELM), and multilayer perceptron (MLP). Moreover, Bayesian optimization (BO) technique is employed for optimal parameter selection of the ML approaches. The stimulation analysis of the HM3-AMRC method is tested on Android malware dataset. Extensive comparison analysis highlighted the betterment of the HM3-AMRC method over existing techniques. © 2023 IEEE.

关键词： Android malware

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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Computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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SourcePlag - Source Code Plagiarism Detection Based on Abstract Syntax Trees

SourcePlag - Source Code Plagiarism Detection Based on Abstr...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Nakul Sharma Siddharth Shinde Swarup Bhosale Suyog Patil Department of Artificial Intelligence and Data Science Faculty of Engineering Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Student of Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Code plagiarism poses a significant challenge in programming communities, necessitating effective detection mechanisms. This paper introduces a novel system that employs Abstract Syntax Trees (ASTs) for code representation and comparison. The system utilizes ASTs to capture the structural essence of code, facilitating a comprehensive analysis of code similarity, it offers flexibility for application across multiple programming languages. In addition to leveraging the Levenshtein Distance Algorithm for Python code comparison, the system incorporates node counting for other languages such as Java and C/C++. By integrating AST-based representation and a combination of comparison techniques, the system offers a robust solution for identifying plagiarized code instances accurately across various programming environments. Through a detailed exploration of the system's methodology, this paper underscores its potential to address the pervasive issue of code plagiarism in programming communities.

关键词： Java Codes Plagiarism Source coding Syntactics Programming Security Reliability Python Testing

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A Novel AI-based Hybrid Ensemble Segmentation CAD System for Lumber Spine Stenosis pathological Regions Using MRI Axial Images 8

A Novel AI-based Hybrid Ensemble Segmentation CAD System for...

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8th International artificial intelligence and data Processing Symposium, IDAP 2024

作者： Salem, Saied Mukhlis, Raza Katar, Oguzhan Ertugrul, Bilal Yildirim, Ozal Al-Antari, Mugahed A. Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of Firat University Faculty of Technology Department of Software Engineering Elazig Turkey Firat University Faculty of Medicine Department of Neurosurgery Elaziǧ Turkey

ISBN: (纸本)9798331531492

Lumbar Spinal Stenosis (LSS) is a condition where the spinal canal in the lower back becomes narrower, resulting in pressure on the spinal cord and nearby nerves. To predict LSS, neurologists focus on four key regions in MRI axial scans: the Intervertebral Disc (IVD), Thecal Sac (TS), Posterior Element (PE), and the Area between the Anterior and Posterior (AAP) vertebrae. To delineate these regions, we present a novel hybrid ensemble deep learning CAD system for multi-class segmentation, encompassing data collection, preprocessing, automated hyperparameter optimization and selection, segmentation ensemble learning, and both qualitative and quantitative performance evaluation. The core of the proposed pipeline features two ensemble phases: (1) ensemble learning through feature space fusion of three distinct AI models (SwinUNETR, Attention Unet, and UNet++), and (2) ensemble learning via a stacking method of the feature maps outputs from the same AI models used in phase 1. The Mendeley benchmark dataset is employed to train and assess both CAD systems, while a private dataset from Firat University Hospital in Turkey is used for verification and validation. The best achieved results via FFA's mean dice score is 93.1% and mean IoU is 87.76%. showing the usefulness of using ensemble methods to capture different features from the trained models. © 2024 IEEE.

关键词： Image segmentation

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Mechanical characterization of extruded sisal-polypropylene composites with optimized compounding parameters

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Interactions 2024年第1期245卷 1-17页

作者： Khawale, Vijay Ramrao Jadhav, Varsha D. Satishkumar, P. Vijayan, D.S. Saminathan, Rajasekaran Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Wanadongri India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Tamilnadu Chennai India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

This study aims to optimize the mechanical characteristics of Sisal-polypropylene composites by investigating the impact of varying the compounding factors on these qualities. These parameters include fiber feeding location, barrel temperature, and fiber content. A capillary rheometer was used to track the changes in viscosity of the mixed compounds in relation to shear rate and temperature. Blends were tested at predetermined shear rates using a Couette shear rate theory–based extruder screw speed chart. In order to determine how each processing variable affected the mechanical characteristics of the composites, an analysis of variance (ANOVA) was conducted, and experimental designs were based on Taguchi’s optimization method. The optimization study revealed that, in addition to barrel temperature, the mechanical characteristics of the specimens were highly influenced by fiber content and feeding position inside the extruder. Compounds containing 40 wt % fiber, when treated at 180 ºC and fed into the extruder’s melt zone, had an average modulus value of 7 GPa and a strength value of 48 MPa. The viscosity measurements show minimal variations at shear speeds above 1850 s− 1, making it suitable for analyzing these substances. The scanning electron micrographs (SEM) reveal that the fibers were coated uniformly with polymer, and there was some seepage through the core, resulting in twofold shear effect. The optimal screw speeds for the blends studied here ranged from 75 to 220 rpm when using the present extruder (Do/Di = 1.3 and shear rates 1850 s− 1 to 4750 s− 1). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Polypropylenes

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Question Categorizer: An Automated Approach For Organizing Queries 2

Question Categorizer: An Automated Approach For Organizing Q...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Roy, Naina Upadhyay, Khushi Doshi, Payal Nagarhalli, Tatwadarshi P. Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India Prime SofTech Solutions Pvt. Ltd. Pune India

ISBN: (纸本)9798350365269

Question categorization is an essential task in natural language processing (NLP) and information retrieval which is the process of automatically assigning a category or class label to a given question based on its content. Categorizing questions plays a pivotal role in enhancing comprehension and facilitating targeted responses. This paper delves into the development and implementation of an advanced question categorization system which classifies questions into eight distinct categories: Analytical, definition, Theoretical, Inferential, Formulation, Differential, Mathematical and Statistical thereby encompassing a wide spectrum of question types. The foundation of this system lies in the integration of a sophisticated BERT architecture, natural language processing techniques, and domain-specific expertise. The system autonomously processes input queries, identifying the underlying characteristics that define each category and assigns the category to the input data. The model is trained on a curated set of data and the proposed methodology achieved promising results with an accuracy score of 0.98 for the 8 categories multiclass classification. © 2024 IEEE.

关键词： Classification (of information)

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Building a Robust Machine Translation System for Punjabi to English Using Deep Learning Approaches 4

Building a Robust Machine Translation System for Punjabi to ...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Nisha Chhabra, Megha Abidi, Ali Imam Setia, Sonia Maurya, Ajay Anjli, Km. Noida Institute of Engineering and Technology Department of Data Science U.P. Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science & Engineering U.P. Greater Noida India Poornima Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Rajasthan Jaipur India Delhi New Delhi India

ISBN: (纸本)9798350387490

In the dynamic realm of artificial intelligence, the evolution of Machine Translation Systems (MTS) for Indian languages faces unique challenges due to linguistic diversity. The diversity of morphology and the inherited dialects make it difficult to translate. The translation can be improved with pre-processing and optimization of MTS. Our MTS proposes the steps to improve the translation of Punjabi to English by employing two models, one with a Gated Recurrent Unit (GRU) layer and another with a Long Short-Term Memory (LSTM) layer. Another step involves Hyperparameter tuning to reduce complexity and increase model's efficiency. The experimentation shows accuracy rate on Samanantar dataset. The research aims to provide solutions to gaps identified in MTS for Punjabi, contributing to the advancement of machine translation technology in Punjabi's linguistic landscape. © 2024 IEEE.

关键词： Long short-term memory

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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LLM4Decompile: Decompiling Binary Code with Large Language Models

LLM4Decompile: Decompiling Binary Code with Large Language M...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tan, Hanzhuo Luo, Qi Li, Jing Zhang, Yuqun Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre for Data Science & Artificial Intelligence Hong Kong

ISBN: (纸本)9798891761643

Decompilation aims to convert binary code to high-level source code, but traditional tools like Ghidra often produce results that are difficult to read and execute. Motivated by the advancements in Large Language Models (LLMs), we propose LLM4Decompile, the first and largest open-source LLM series (1.3B to 33B) trained to decompile binary code. We optimize the LLM training process and introduce the LLM4Decompile-End models to decompile binary directly. The resulting models significantly outperform GPT-4o and Ghidra on the HumanEval and ExeBench benchmarks over 100% in terms of re-executability rate. Additionally, we improve the standard refinement approach to fine-tune the LLM4DecompileRef models, enabling them to effectively refine the decompiled code from Ghidra and achieve a further 16.2% improvement over the LLM4Decompile-End. LLM4Decompile demonstrates the potential of LLMs to revolutionize binary code decompilation, delivering remarkable improvements in readability and executability while complementing conventional tools for optimal results. © 2024 Association for Computational Linguistics.

关键词： Binary codes

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