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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Preethi, T. Reddy, Ponnuru Rakshitha Likhitha, Lekkala Kumar, Pendyala Pavan Kamani, Abhinav Vnr Vignana Jyothi Institute of Engineering and Technology Data Science and Artificial Intelligence & Data Science Department of Cse - Cyber Security Hyderabad India

ISBN: (纸本)9789380544519

In today's digital world, it is crucial to keep a company's information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort's capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Decision Tree Intrusion Detection System K-Nearest Neighbours (KNN) Machine Learning Naive Bayes NSL-KDD dataset Random Forest Snort Support Vector Machine (SVM)

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Artificial Intelligence Support for the Prompt Identification and Understanding of the Broader Spectrum of Autism in Children 5

Artificial Intelligence Support for the Prompt Identificatio...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Julaiha, A.G. Noorul Hemamalini, S. Preiya, V. Sathya Sathyamoorthy, K. Priyanka, V. Rajalakshmi Institute of Technology Centre for Data Science Department of Computer Science & Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

This research work presents a novel language intervention system for Tamil-speaking children with autism spectrum disorder (ASD). The system satisfies the considerable requirement for tools aimed at one more section of population that has actually been forgotten by mainstream economics. Traditional language learning applications often lack specificity for diverse linguistic contexts, focusing on general learning rather than the unique challenges faced by ASD children. The proposed solution leverages Deep Learning (DL) modules to transform partial spoken Tamil words into complete, child-friendly vocabulary. This system goes beyond simple word completion;it incorporates interactive features to engage children with ASD and promote active learning. Preliminary evaluations yielded promising results, suggesting the system's potential in enhancing communication skills among Tamil-speaking children with ASD. This paper research study reviews the limitations of existing language learning tools and the rationale behind employing a DL-based approach. We discuss the specific DL architecture (e.g., Recurrent Neural Networks) envisioned for the system and how it will be trained on a corpus of Tamil speech data. This study also investigate the potential advantages beyond vocabulary learning, such as improved sentence formulation and the development of practical language abilities. Future ambitions include broadening the system's scope to include more languages, refining the DL model through ongoing training with enriched datasets, and conducting extensive user research with varied ASD populations. Validation across demographics is critical to ensuring the system's efficiency and generality. This research has the potential to bridge the language acquisition gap for Tamil-speaking children with ASD, empowering them to develop crucial communication skills and fostering their social inclusion. © 2024 IEEE.

关键词： Recurrent neural networks

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Effectiveness of a touch-based hand therapeutic application for hand dexterity in children with handwriting difficulties-a pilot study

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Multimedia Tools and Applications 2024年 1-35页

作者： Smitha, John Renumol, V.G. School of Computer Science and Technology Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India School of Engineering Cochin University of Science and Technology Kochi India

Handwriting is an important skill for children during their academic years. It is the coordination of perceptual-motor and cognitive abilities. Some children have difficulties in handwriting, which is an indication of issues in their fine motor skills. Nowadays, technology-based instructions are available to develop fine motor skills in children with handwriting disabilities. This paper describes the details of a newly developed software application namely ‘HanDex’ for children with handwriting difficulties and the experiments conducted to test its effectiveness. The application comprises six hand therapeutic activities which were designed based on the oscillation theory of handwriting. Initially, in the first phase, the experiments were conducted in nine primary school children from various schools, aged 8–12 years. Then, in the second phase, the software training was given to six upper primary school children (10–12 years) with handwriting issues. The results of using this application were promising. So one more study was conducted in another set of nine children (8–11 years) with handwriting difficulties. The participants were assessed by their special educators, class teachers and parents. After the pretest, they received a total of 18 handwriting practice sessions using this new application. At the end of the training sessions, a post test was conducted, to measure the progress in handwriting. The handwriting rubrics with eight handwriting parameters such as letter formation, placement, letter sizing, spacing of letters, legibility, speed, neatness, and spacing of words were used for handwriting assessment. The findings from the study suggested that touch-based hand therapeutic activities were beneficial for children with writing difficulties to improve their handwriting performance and tripod grip. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Children with handwriting difficulties Hand Dexterity Handwriting performance and tripod grip Tablet computer ‘HanDex’ Software

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An Elevator Button Recognition Method Combining YOLOv5 and OCR

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Computers, Materials & Continua 2023年第4期75卷 117-131页

作者： Xinliang Tang Caixing Wang Jingfang Su Cecilia Taylor School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Nankai-Birmingham Institute of Data Science Intelligence BirminghamB100ABBritain

Fast recognition of elevator buttons is a key step for service robots toride elevators automatically. Although there are some studies in this field, noneof them can achieve real-time application due to problems such as recognitionspeed and algorithm complexity. Elevator button recognition is a comprehensiveproblem. Not only does it need to detect the position of multiple buttonsat the same time, but also needs to accurately identify the characters on eachbutton. The latest version 5 of you only look once algorithm (YOLOv5) hasthe fastest reasoning speed and can be used for detecting multiple objects inreal-time. The advantages ofYOLOv5 make it an ideal choice for detecting theposition of multiple buttons in an elevator, but it’s not good at specific wordrecognition. Optical character recognition (OCR) is a well-known techniquefor character recognition. This paper innovatively improved the YOLOv5network, integrated OCR technology, and applied them to the elevator buttonrecognition process. First, we changed the detection scale in the YOLOv5network and only maintained the detection scales of 40 ∗ 40 and 80 ∗ 80, thusimproving the overall object detection speed. Then, we put a modified OCRbranch after the YOLOv5 network to identify the numbers on the ***, we verified this method on different datasets and compared it withother typical methods. The results show that the average recall and precisionof this method are 81.2% and 92.4%. Compared with others, the accuracyof this method has reached a very high level, but the recognition speed hasreached 0.056 s, which is far higher than other methods.

关键词： Button recognition deep learning multi-object detection

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First-principles study on the interfacial bonding strength and segregation at Mg/MgZn_(2)matrix interface

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Journal of Magnesium and Alloys 2024年第10期12卷 4053-4062页

作者： Longke Bao Peng Du Shengkun Xi Cuiping Wang Kaihong Zheng Rongpei Shi Guoqiang Xie Xingjun Liu State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering and Institute of Materials Genome and Big DataHarbin Institute of TechnologyShenzhen 518055PR China College of Materials Science and Engineering Xiamen UniversityXiamen 361005PR China Institute of New Materials Guangdong Academy of SciencesGuangzhou 510650PR China

To understand the interface characteristics between the precipitateβ2'and the Mg matrix,and thus guide the development of new Mg-Zn alloys,we investigated the atomic interface structure,work of adhesion(Wad),and interfacial energy(γ)of Mg(0001)/β2’(MgZn_(2))(0001)interface,as well as the effect of segregation behavior of the introduced transition metal atoms(3d,4d and 5d)on interfacial bonding *** calculated works of adhesion and interfacial energies dementated that the Zn2-terminated MT+HCP configuration is the most stable structure for all considered *** the Zn2-MT+HCP interface as the research object,estimated segregated energies(Eseg)reveal that added transition metal atoms prefer to segregate at Mg-I and Mg-II *** predicted Wad and charge density difference results reveal that the segregation of alloying additives employed may all strengthen Mg(0001)/MgZn_(2)(0001)interface,with the enhancement effect of Os,Re,Tc,W,and Ru at the Mg-II site being the most pronounced.

关键词： Mg/MgZn_(2)interface Work of adhesion Interfacial energy Interface segregation

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Large language models design sequencedefined macromolecules via evolutionary optimization

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npj Computational Materials 2024年第1期10卷 446-453页

作者： Wesley F.Reinhart Antonia Statt Department of Materials Science and Engineering Pennsylvania State UniversityUniversity Park16802 PAUSA Institute for Computational and Data Sciences Pennsylvania State UniversityUniversity Park16802 PAUSA Department of Materials Science and Engineering Grainger College of EngineeringUniversity of Illinois Urbana-ChampaignChampaign61801 ILUSA

We demonstrate the ability of a large language model to perform evolutionary optimization for materials ***’s Claude 3.5 model outperforms an active learning scheme with handcrafted surrogate models and an evolutionary algorithm in selecting monomer sequences to produce targeted morphologies in macromolecular *** pre-trained language models can potentially reduce the need for hyperparameter tuning while offering new capabilities such as *** model performs this task effectively with orwithout context about the task itself,but domain-specific context sometimes results in faster convergence to good ***,when this context is withheld,the model infers an approximate notion of the task(e.g.,calling it a protein folding problem).This work provides evidence of Claude 3.5’s ability to act as an evolutionary optimizer,a recently discovered emergent behavior of large language models,and demonstrates a practical use case in the study and design of soft materials.

关键词： offering evolutionary faster

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Ensemble of deep learning models with Walrus Optimization Algorithm for accurate botnet recognition and classification

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Iran Journal of Computer science 2025年 1-18页

作者： Chacko, Ashwathy Anda Edwin, E. Bijolin Thanka, M. Roshni Ebenezer, V. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India

The rapid growth of Internet of Things (IoT) networks has introduced significant security challenges, with botnet attacks being one of the most prevalent threats. These attacks exploit vulnerabilities in IoT devices, leading to severe disruptions and damage to critical infrastructures. Detecting botnet attacks in IoT environments is challenging due to the large volume of data, the dynamic nature of traffic, and the diverse attack patterns. To address these issues, we propose a novel approach called Walrus Optimized Ensemble Deep Learning for Anomaly-Based Recognition Classifier (WOAEDL-ABRC), which leverages a combination of advanced machine learning techniques for effective botnet detection. The methodology of this research involves four key components: (1) data preprocessing through min–max normalization to scale the features appropriately, (2) feature selection using the social cooperation search algorithm (SCSA) to identify the most informative attributes, (3) an ensemble deep learning model combining convolutional autoencoder (CAE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN) for robust anomaly detection, and (4) hyperparameter optimization using the Walrus Optimization Algorithm (WAOA), which fine-tunes the model parameters for optimal performance. This ensemble approach ensures that the model benefits from the strengths of each individual technique while mitigating the weaknesses of others. The dataset used for this research includes network traffic data from IoT environments, consisting of various botnet attack scenarios and normal traffic patterns. The data undergoes extensive preprocessing and feature selection to reduce dimensionality and enhance the model’s performance. The implementation is carried out in Python using TensorFlow for deep learning, with the WAOA applied to optimize hyperparameters. The results demonstrate the effectiveness of the WOAEDL-ABRC in detecting botnet attacks, achieving superior accuracy, precision

关键词： Botnet recognition Deep learning Feature selection Internet of things Walrus Optimization Algorithm

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Multi-Disease Prediction and Classifier: A Comprehensive Approach for Healthcare Decision Support 5

Multi-Disease Prediction and Classifier: A Comprehensive App...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sudha, S.V. Priya, Kamali S. Krishnapriya, P. Prathiksha, P.N. Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350361155

The speedy identification and early detection of diseases that are deadly are greatly important when it comes to human life saving. On the other hand, the existence of poor medical system alongside the lack of equal distribution of healthcare presents a barrier towards early disease detection leaving death as the only option. One of the ways to tackle these problems is the utilization of machine learning in detection and analysis of diseases risk is machine learning promising. Utilizing predictive analytics in medicine, practitioners can make accurate and relevant medical insights promptly, relying on the information they have. The work focused on generating prediction models to identify seven most severe illnesses: diabetes, pneumonia, malaria, heart disease, kidney disease, breast cancer, and liver disease, using an algorithm of Random Forest Classifier. Alongside this, the Convolutional Neural Networks were utilized for the detection of malaria and pneumonia. Users can get better knowledge about their health problems and at the same time reduce the access constraints to healthcare by using machine learning. This research is also about designing a web app with the Flask framework and will give its consumers an easy-to-use tool for the simultaneous prediction of all seven diseases. Such a user interface grants better healthcare access and results in the use of proactive measures, as people can take a more active role in their wellness. © 2024 IEEE.

关键词： Diseases

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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