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Fuzzing:Progress,Challenges,and Perspectives

computers, Materials & Continua 2024年第1期78卷 1-29页

作者： Zhenhua Yu Zhengqi Liu Xuya Cong Xiaobo Li Li Yin Institute of Systems Security and Control College of Computer Science and TechnologyXi'an University of Science and TechnologyXi'an710054China School of Mathematics and Information Science Baoji University of Arts and SciencesBaoji721013China Institute of Systems Engineering Macao University of Science and TechnologyTaipaMacaoChina

As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software *** feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the *** recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the *** paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its ***,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.

关键词： Fuzzing vulnerability software testing software security

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Integration of smart Sensors and Machine Learning Algorithms for Automated Pest Management 1

Integration of smart Sensors and Machine Learning Algorithms...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Yadav, Krrish Roy, Sritama Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798331520540

This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pesticides. The proposed system solves this problem by predicting pest populations using environmental data collected via IoT sensors. We use random forest regression and random search cross-validation to predict pests and identify important factors. The system calculates the amount of pesticides based on forecasts, helping farmers optimize use and reduce environmental impact. The experimental results demonstrate the effectiveness of the system in terms of accurate prediction and optimal pesticide application, and thereby providing a scalable and cost-effective solution for sustainable agriculture. © 2025 IEEE.

关键词： Smart agriculture

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Smart Parking System using YOLO, ANPR and MQTT with Artificial Intelligence Features 3

Smart Parking System using YOLO, ANPR and MQTT with Artifici...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Yadav, Krrish Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798331527549

Smart parking systems leverage advanced technologies to optimize parking space utilization and enhance user experience. This research paper explores the design, implementation, and evaluation of a smart parking system aimed at improving accessibility and security in urban parking facilities. It utilizes real-time sensor data to provide accurate parking availability information and empowers users to book slots through a secure mobile web app. The app integrates user-friendly navigation to guide drivers to their booked spots. OTP generation bolsters security, preventing unauthorized parking occupancy. One of the most significant and difficult problems in image-based sequence recognition is scene text recognition, which is the focus of this study's investigation. A brand-new neural network architecture is put forth that unifies transcription, sequence modelling, and feature extraction into a single framework. It performs admirably in both lexicon-free and lexicon-based scene text recognition tasks and is not restricted to any predefined lexicon. It produces a much more manageable and effective model, making it more suitable for real-world application scenarios. © 2025 IEEE.

关键词： App development Neural network OTP generation Smart parking text recognition

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A Comprehensive Study on Advancements in Text Mining and Natural Language Processing 2

A Comprehensive Study on Advancements in Text Mining and Nat...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Mahire, Sarthak Balaji Saravanan, U.K. Murali, M. Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

Text mining and Natural Language Processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Ensembles of Pre-trained CNNs for Improved Lung Cancer Detection and Classification

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Procedia computer science 2025年 260卷 151-159页

作者： Dasari Bhulakshmi Dharmendra singh rajput Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore 632014 India

Lung cancer is a serious global health concern, highlighting the importance of early identification to improve patient survival rates. We explore the potential of deep learning(DL) models to improve lung cancer diagnosis through detection and classification models. The performance of pre-trained ResNet50, VGG19, and AlexNet models is evaluated on an augmented lung cancer image dataset to determine their suitability for lung cancer classification. The fine-tuned models are evaluated for their ability to identify and classify lung cancer, achieving high accuracy of 92.88%, 93.06%, and 95.23%. While promising, this approach has limitations. The efficacy of DL models is significantly influenced by both the quality and volume of the training data. Additionally, the ”black box” nature of DL models can make it challenging to understand their decision-making process. However, the results of this study suggest that DL ensembles hold significant potential for lung cancer diagnosis. Further research is necessary to address limitations and explore interpretability techniques for wider clinical acceptance.

关键词： Deep Learning Machine Learning convolutional neural networks Artificial Intelligence

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Interactive Learning with AlphAR: A Symbol-Recognition Aid for Dyslexic Children Through Augmented Reality

Interactive Learning with AlphAR: A Symbol-Recognition Aid f...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Shilbi, Susan Chhirolya, Vaidik Basha, Niha Kamal Surendiran, B. School of Computer Science and Engineering Vellore Institute of Technology Vellore India National Institute of Technology Puducherry Department of Computer Science and Engineering Karikal India

ISBN: (纸本)9798331540685

Dyslexia is a neurological condition that affects a person's ability to read, write, and spell. It is characterized by difficulties in processing phonological information, leading to challenges in acquiring fluent and accurate reading skills. Early assistance fosters a positive learning trajectory, enhancing the overall well-being and confidence of dyslexic individuals. AlphAR is an innovative augmented reality (AR) web system specifically tailored to address the challenges of symbol recognition in dyslexic children, developed using cutting-edge A-Frame and *** technologies. It harnesses marker-based AR to create an immersive learning environment. The novelty lies in its seamless integration of marker patterns with corresponding 3D models, facilitating interactive learning experiences for children aged 5 to 10. Through intuitive user interface elements and audio cues, AlphAR enhances engagement and promotes auditory learning. Ethical design principles ensure inclusivity and user-friendliness, prioritizing participant comfort and engagement. Users interact with the system through intuitive gestures, such as flipping down and left, further enhancing skill development. The iterative nature of AlphAR allows for adaptability to individual learning paces, fostering a positive and inclusive experimental environment. To validate AlphAR's effectiveness, we will benchmark it against established AR interventions like LitAR, AHA System and many others. © 2024 IEEE.

关键词： Contrastive Learning

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Prognostic Survival Analysis for AD Diagnosis and Progression Using MRI Data: An AI-Based Approach

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IEEE Access 2025年 13卷 89059-89078页

作者： Deenadayalan, T. Shantharajah, S.P. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore632014 India

Alzheimer’s Disease is a progressive neuro-degenerative disorder and a leading cause of dementia, marked by cognitive decline, memory loss, and behavioral changes. Despite advancements in medical imaging and Artificial Intelligence (AI), early detection and accurate prognostic modeling remain challenging, particularly in distinguishing overlapping disease stages. This study proposes a novel AI framework combining EfficientNetB0, a state-of-the-art deep learning model, with dual attention mechanisms for robust feature extraction and survival analysis using the Alzheimer’s MRI dataset hosted on the Kaggle platform. The model was trained and evaluated on a balanced dataset of 6,400 MRI images, classified into 4 AD stages: Non-Demented (NoD), very Mild-Demented (vMiD), Mild-Demented (MiD), and Moderate-Demented (MoD). The pre-processing steps, including resizing, normalization, and data augmentation, ensured robustness and generalizability. The framework achieved classification accuracies of 99. 93% (training), 93. 60% (validation) and 93. 59% (testing), with a perfect Concordance Index of 1.0 for survival predictions, demonstrating its ability to accurately rank survival times. By integrating survival analysis with deep learning, the framework addresses limitations in previous work, such as reliance on linear assumptions and limited classification capabilities, and provides actionable insights for clinical workflows, supporting personalized treatment and early intervention strategies. Future work will focus on integrating longitudinal data, exploring time-dependent co-variates, and enhancing explainability to promote adoption in diverse clinical settings. This approach represents a significant advancement in AI-driven diagnosis and progression modeling of AD, with the potential to transform patient care and outcomes. © 2013 IEEE.

关键词： Deep learning

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Deep Learning Techniques for Weather Prediction

Deep Learning Techniques for Weather Prediction

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhanalakshmi, R. Paul, Tushar School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Masters of Computer Application Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331510022

Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Precautions against such climatic calamities require weather forecast models that are both accurate and effective. As a result, creating models that accurately forecast the weather is crucial. The creation of effective models of deep learning-based weather forecasting was greatly aided by technological advancements. This study suggested a hybrid weather prediction model based on convolutional and recurrent neural networks, which had a decent success rate. The dataset named Jena, which comprises 14 parameters, extensive meteorological information that has not been used for predicting the weather, was subjected to the suggested hybrid model. The experimental findings were compared using well-known machine learning, deep learning, and statistical techniques such CNN, Integrated Moving Average with Auto-Regression, LSTM, RNN, Random Forest, Multilayer Perceptrons and SVM. These comparisons showed that the suggested hybrid model had the best prediction outcome across the board. For instance, the suggested hybrid model produced the following findings based on the weather forecast data for Jena. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Helmet And Monitoring For Miners With Enhanced Protection 2

Smart Helmet And Monitoring For Miners With Enhanced Protect...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Banik, Arnob Mishra, Divakar Manikandan, N. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India

ISBN: (纸本)9798350347579

Internet of Things (IoT) has lately been expanded across various applications, drawing significant attention to its design, where a loud industrial atmosphere can exist in the mining area. The central essence of this project was to detect hazardous events and to design a perfect, smart helmet that ensures the safety of workers. We have decided to implement a system that could be embedded in the helmets of the mine workers. Workers would be alerted via a helmet buzzer, so necessary action was taken on time. The critical feature of this project was that the model presented uses an application interface to detect real-time data and display it. The administrators outside may monitor the dynamic data from the system using a website and a mobile application. In contrast, the Android app displays or informs the data or alerts. The miners and the administrators can monitor real-time data that constantly changes according to the changes in the environment. They could also monitor the changes in temperature and humidity through graphs continuously updated on the applications. 'Rescue with Flexibility' was the main motto of our project. Thus, this project offers a directional platform that would bring a revolutionary change in this industry. © 2023 IEEE.

关键词： Internet of things

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