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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kumar, Sandeep Gupta, Bhupesh Chhabra, Mohit Garg, Lucy Department of Computer Science & Engineering Mullana India Panipat Institute of Engineering Technology Department of Computer Science & Engineering Panipat India

ISBN: (纸本)9798350376470

A fruit valued for its great flavor, scent, and nutritional content;the mango (Mangifera indica L.) is one of the most significant tropical fruits in the world economically. However, several illnesses that compromise fruit quality and output significantly hinder the commercial cultivation and trading of mangoes. This widespread illness has significant financial consequences and represents a danger to mango farming worldwide. A key component of effectively managing the illness and protecting crops is the timely and accurate assessment of the disease's severity. The message highlights how critical it is to detect illnesses as soon as possible and with precision to enable effective crop management. The main illnesses that afflict mango fruits are covered in detail in this document, along with information on their genesis, symptoms, epidemiology, and management techniques. The primary disorders covered are anthracnose, mango malformation, powdery mildew, mango bacterial black spot, and mango abrupt decline (mango decline-associated virus). Every disease is thoroughly investigated, covering its causes, methods of spread, symptoms, and effects on fruit output and quality. Machine learning (ML) approaches have become more effective tools for agricultural system disease detection and categorization in recent years. To shed light on the efficacy and relevance of machine learning (ML) algorithms for categorizing mango fruit illnesses, this research study compares and contrasts them. © 2024 IEEE.

关键词： Fruits

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BD-Trust: behavioural and data trust management scheme for internet of things

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16195-16207页

作者： Sharma, Avani Pilli, Emmanuel S. Mazumdar, Arka P. Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

The emergence of Internet of Things (IoT) has paved the way to realize ubiquitous access to manifold services and applications. Effective utilization of these services vitally demands secure deployment of IoT systems where data or information can be transmitted without worrying about the uncertainty of different devices in the system. Trust Management Systems (TMS) have proved themself as impressive solutions to attain this security requirement where entities are judged based on their behavior. For IoT, influential deployment of TMS requires coping with characteristics of IoT systems such as device heterogeneity, context-awareness, dynamicity, etc. Paying attention to the quality of received data for reliable service composition and accurate assessment is also a matter of concern. This paper presents a direct observation-based distributed trust management scheme, called BD-Trust, in which trust is measured by integrating behavioral trust of devices with the data trust. For evaluation of behavioral trust, BD-Trust reckons on three trust parameters, namely success rate, satisfaction level, and credibility. Data trust is measured considering consistency in received data values for the successful interaction responses. BD-Trust employs a trust update mechanism to alleviate the effect of malicious devices that try to disrupt the process of trust management. An adaptive decay factor has been designed to reflect previously observed trust value in recent computation while updating trust scores. The effectiveness of BD-Trust has been validated through simulation experiments and results have been compared with two state-of-art schemes for trust convergence, assessment accuracy, and resilience against attacks, considering a dynamic environment. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

An Intelligent Analysis of Mobile Evidence Using Sentimental...

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International Conference on Information Security, Privacy, and Digital Forensics, 2022

作者： Jones, G. Maria Santhiya, P. Winster, S. Godfrey Sundar, R. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology and Panimalar Engineering College Chennai India

ISBN: (纸本)9789819950904

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Sensitive information leakage analysis of database code by abstract interpretation

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International Journal of Security and Networks 2023年第2期18卷 91-105页

作者： Jana, Angshuman Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India

An information system stores outside data in the backend database to process them efficiently and protects sensitive data from illegitimate flow or unauthorised users. However, most information systems are made in such a way that the sensitive information stored in a database may be leaked explicitly or implicitly during data processing along with the control structure of the program to the output channels. Therefore, sensitive data leakage is one of the crucial security threat. In this paper, the main objective is to detect the illegitimate flow of confidential information in an information system. We propose a framework to detect sensitive information leakage through the data-flow paths of an information system. In particular, to compute the precise set of data-flow paths, we use the non-relational abstract property of the interval domain and the relational abstract property of the polyhedra domain that enables the framework to produce efficient security analysis results. © 2023 Inderscience Enterprises Ltd.

关键词： Formal methods

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LightSTATE: A Generalized Framework for Real-Time Human Activity Detection Using Edge-Based Video Processing and Vision Language Models

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IEEE Access 2025年 13卷 97609-97627页

作者： Debnath, Anik Kim, Yong-Woon Byun, Yung-Cheol Jeju National University Department of Computer Engineering Jeju-si63243 Korea Republic of Jeju National University Institute of Information Science & Technology Department of Computer Engineering Major of Electronic Engineering Jeju63243 Korea Republic of

Human activity detection plays a vital role in applications such as healthcare monitoring, smart environments, and security surveillance. However, traditional methods often rely on computationally intensive models, which are unsuitable for edge devices due to their limited resources and high latency. This research introduces a new approach named LightSTATE, a lightweight hybrid framework designed for real-time activity detection by integrating edge-based preprocessing with cloud-hosted Vision Language Models (VLMs). The system processes video streams through efficient frame extraction, edge detection and Root Mean Square Error (RMSE)-based frame filtering to identify significant motion events. These selected frames are transmitted to the cloud for semantic analysis and state recognition. The system utilizes GPT-4o as the vision-language model to generate rich, descriptive captions for human state understanding. LightSTATE was evaluated on a curated dataset of 9 real-world videos containing 634 frames, with 320 labeled frames used for testing. The framework demonstrated high accuracy in detecting predefined states, with an average precision of 0.98, an average recall of 0.98, and an average F1-score of 0.98. The experimental results confirm that the average frame processing time is approximately 0.8-0.9 seconds, ensuring suitability for real-time applications. Due to the scarcity of publicly accessible datasets, we compiled a custom dataset to effectively assess the performance of our proposed approach. The results validate the scalability and real-time capabilities of our proposed system, making it a promising solution for continuous monitoring applications. This study establishes LightSTATE as a scalable, efficient, and privacy-preserving framework for real-time human activity detection. Future work will focus on extending its applicability to dynamic and multi-person scenarios while enhancing system adaptability for diverse use cases. © 2013 IEEE.

关键词： Edge detection

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Deep Learning System and It’s Automatic Testing: An Approach

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Annals of Data science 2023年第4期10卷 1019-1033页

作者： Khan, Rijwan Department of Computer Science and Engineering ABES Institute of Technology UP Ghaziabad India

The process of testing conventional programs is quite easy as compared to the programs using Deep Learning approach. The term Deep learning (DL) is used for a novel programming approach that is highly data centric and where the governing rules and logic are primarily dependent on the data used for training. Conventionally, Deep Learning models are evaluated by using a test dataset to evaluate their performance against set parameters. The difference in data and logic handling between programs using conventional methods and programs using the DL approach makes it difficult to apply the traditional approaches of testing directly to DL based programs. The accuracy of test data is currently the best measure of the adequacy of testing in the DL based systems. This poses a problem because of the difficulty in availability of test data that is of sufficient quality. This in turn restricts the level of confidence that can be established on the adequacy of testing of DL based systems. Unlike conventional applications, using the conventional programming approaches the lack of quality test data and the lack of interpretability makes the system analysis and detection of defects a difficult task in DL based systems. So testing of DL based models can be done automatically with a different approach compared to normal software. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automatic testing Deep learning system Automatic test case generation Software testing Unit testing

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YOLOv5-based weapon detection systems with data augmentation

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International Journal of computers and Applications 2023年第4期45卷 288-296页

作者： Sumi, Lucy Dey, Shouvik Department of Computer Science and Engineering National Institute of Technology Nagaland Chumukedima India

Closed-Circuit Television (CCTV) cameras in public places have become more prominent with the rising firearm-related criminal activities, such as robberies, open firing, threats at gunpoint, etc. Early detection of firearms in surveillance systems is crucial for security and safety concerns. In this paper, we present a You Only Look Once (YOLOv5)-based weapon detection system that detects different types of weapons such as rifles, pistols, knives, etc. The main objective of this work is to show the impact of data augmentation on different types of datasets and make a detailed comparative examination with the existing baseline study and other similar works in the literature. The results give new insights to consider for weapon detection systems and object detection, in general. A crisp taxonomy of the existing state-of-the-art and object detection trends over the past decades is also presented in the paper. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Object detection

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Fuzzy modelling techniques for improving multi-label classification of software bugs

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International Journal of Innovative Computing and Applications 2023年第3期14卷 141-154页

作者： Panda, Rama Ranjan Nagwani, Naresh Kumar Department of Computer Science and Engineering National Institute of Technology Chhattisgarh Raipur India

Software bug repositories stores a wealth of information related to the problems that occurred during the software development. Today’s software development is a modular approach, with multiple developers working in different locations all around the world. A software bug may belong to multiple categories and can be resolved by more than one developer. For understanding the multiple causes of software bugs and proper bug information management at large bug repositories, better classification of software bugs is needed. In the proposed work, a multi-label fuzzy system-based classification (ML-FBC) is proposed. A fuzzy system is used to compute the membership of software bugs into multiple categories. Then a fuzzy c-means clustering algorithm is used to create various clusters. Once the clusters are created, the cluster-category mapping is done for various software bugs. For a new bug, the fuzzy similarity values are computed, and the created cluster-category mappings are utilised to categorise it. Using a user-defined threshold value, a new bug is classified into multi-label categories. Experiments are carried out on available benchmark datasets to compare the performance measures F1 score, BEP score, Hloss, accuracy, training time, and testing time of various multi-label classifiers. The proposed ML-FBC outperforms existing multi-label classifiers. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Information management

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning 3

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Gopal, Vidhya Surendar, U. Bajulunisha, A. Tamilselvi, M. Sathiyapriya, V. Saranya, N. Sona College of Technology Department of Computer Science and Engineering Salem India Srm institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331508456

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST-WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment. © 2025 IEEE.

关键词： Reinforcement learning

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A Survey of Stock Market Prediction - Based on Machine Learning Techniques 2

A Survey of Stock Market Prediction - Based on Machine Learn...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kangana, W.M. Allagi, Shridhar Laddi, Mahantesh S G Balekundri Institute of Technology Department of Computer Science and Engineering Belagavi India Kle Institute of Technology Department of Computer Science and Engineering Hubli India Bharatesh Institute of Technology Department of Computer Science and Engineering Belagavi India

ISBN: (纸本)9798350388916

Stock market Prediction has been a topic of attention for numerous researchers since its beginning. Often traditional statistical methods get conflict to grab the complex, non-linear patterns in stock market data. Due to various factors predicting stock prices is challenging, including a company's financial performance, present assets, politics, general economic conditions, politics, unanticipated occurrence, global situation, etc. The fluctuating behavior of the inventory place market makes it difficult to expect stock prices, as they constantly change. This paper comprehensively reviews machine and deep learning techniques used to predict the stock market and highlights the proper techniques for predicting inventory cost. This paper also covers the performance metrics for measuring the performance of prediction models, research gaps, and issues found in previous work. © 2024 IEEE.

关键词： Marketplaces

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