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

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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Deep Learning based Air Quality Forecasting using Satellite data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. This research focuses on the development of an advanced air quality forecasting system using machine learning techniques. The system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. The model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. The system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. This research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Semantic deep learning and adaptive clustering for handling multimodal multimedia information retrieval

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Multimedia Tools and Applications 2025年第13期84卷 11795-11831页

作者： Sattari, Saeid Yazici, Adnan Computer Engineering Department Middle East Technical University Ankara06531 Turkey Computer Science Department Nazarbayev University Astana010000 Kazakhstan

Multimedia data encompasses various modalities, including audio, visual, and text, necessitating the development of robust retrieval methods capable of harnessing these modalities to extract and retrieve semantic information from multimedia sources. This paper presents a highly scalable and versatile end-to-end framework for multimodal multimedia information retrieval. The core strength of this system lies in its capacity to learn semantic contexts within individual modalities and across different modalities, achieved through the utilization of deep neural models. These models are trained using combinations of queries and relevant shots obtained from query logs. One of the distinguishing features of this framework is its ability to create shot templates, representing videos that have not been encountered previously. To enhance retrieval performance, the system employs clustering techniques to retrieve shots similar to these templates. To address the inherent uncertainty in multimodal concepts, an improved variant of fuzzy clustering is applied. Additionally, a fusion method incorporating an OWA operator is introduced. This method employs various measures to aggregate ranked lists produced by multiple retrieval systems. The proposed approach leverages parallel processing and transfer learning to extract features from three distinct modalities, ensuring the adaptability and scalability of the framework. To assess its effectiveness and efficiency, the system is rigorously evaluated through experiments conducted on six widely recognized multimodal datasets. Remarkably, our approach outperforms previous studies in the literature on four of these datasets, achieving performance improvements ranging from 1.5% to 10.1% over the best reported results in those studies. The experimental findings, substantiated by statistical tests, conclusively establish the effectiveness of the proposed approach in the field of multimodal multimedia information retrieval. © The Author(s), und

关键词： Fuzzy clustering

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An Intelligent Vehicle Control System for Enhancing Road Safety Using Optimal Visible Light Communication Network

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Journal of Optical Communications 2023年第1期44卷 81-94页

作者： Begum, Shaeista Patil, Nagaraj B. Department of Computer Science and Engineering Government Engineering College Karnataka Raichur India

A visible light communication (VLC) provides potential and effective communication paradigm due to the demand of high data-rate applications. VLC networks, consisting of multiple light emitting diodes (LEDs) and it provides the low-cost high data-rate transmission to multiple users simultaneously in indoor environments. VLC has been recently introduced as a secure directional data transmission in vehicle to vehicle to provide an intelligent vehicle control system. However, the performance of this system is mostly affected by the collision of data transmission between different users. In this paper, we introduce an optimal visible light communication (OVLC) network that allows vehicles which have provides collision aware data transmission to improve the chance of transmitting information successively according to the network condition. Firstly, the next forwarding node is selected by the chaotic fish swarm optimization (CFSO) algorithm with the help of vehicle information's such as intensity of light, the distance and speed of neighboring vehicles. The second contribution is to illustrate the congestion control (CC) system for avoiding extra time due to the control packets exchange process. The optimal result is then forward to the source vehicle equipped device, which helps the driver to make a healthy to control vehicle and efficiently avoid or prevent road accidents under different circumstances. The results show that the proposed OVLC network performs very efficient than existing network in terms of quality metrics, such as throughput, delay, packet loss rate, energy consumption and fairness index. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Visible light communication

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Analyzing Classroom Behavior to Enhance Student Engagement and Learning Outcomes 2

Analyzing Classroom Behavior to Enhance Student Engagement a...

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

作者： Suresh Kumar, S. Sivaprakasam, Anantha Joseph, R Edwin Sandeep, Atmakuru Siva Buvankalyan, P. Rajalakshmi Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Automobile Engineering Chennai India

ISBN: (纸本)9798331534950

This research presents a high-tech smart system that utilizes video records to monitor student behavior in classroom settings and identify specific activities like raising hands and dozing. The uneven frequency of certain behaviors along with variances in student posture, seating arrangements, and video quality make it extremely difficult to identify these behaviors in a live classroom. While some behaviors, like raising one's hand, may happen frequently and in a variety of ways, others, like napping, may happen less frequently but are just as crucial to comprehending student engagement. We use a sizable annotated dataset, in which bounding boxes are constructed around significant objects of interest in the video frames, to get over these difficulties. Our system's core is a convolutional neural network (CNN), which we have specially tuned for behavior identification. The CNN model is made to quickly evaluate the visual data and recognize characteristics and patterns connected to certain student behaviors in real time. We make sure the model can accurately identify actions like raising one's hand and falling asleep even when there are variations in lighting, seating configurations, and video quality by training it with a large dataset. Our model is specifically designed to achieve high accuracy in identifying minute cues that distinguish one movement from another, in contrast to conventional methods that might find it difficult to comprehend the intricacy of these actions. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhanced Wi-Fi-Li-Fi system

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 183-189页

作者： Gowtham, M. Department of Computer Science and Engineering The National Institute of Engineering Karnataka Mysuru India

Today's world is fully dependent on data. data are individual packets or units of information which on process leads to a useful information which intend helps in decision making. So these data are to be shared among the multiple user devices and we come across some of the most commonly used transmission media. Currently, Wireless Fidelity (Wi-Fi) is dominating over other media because of its ease of remote accessing methods without any physical connection. But these do have some loopholes, as they have limited speed of transmission or very low bit rate. So in order to overcome these drawbacks we come up with an emerging concept where the speed of transmission can be enhanced up to 100 Gbps with the help of light as the source of transmission, called Light-Fidelity (Li-Fi). Li-Fi range is of large bandwidth that enables the communication in radiofrequency areas and shows data transmission efficiently. By using both Wi-Fi and Li-Fi we propose a new system, the hybrid Wi-Fi-Li-Fi system. © 2023 Inderscience Enterprises Ltd.

关键词： Decision making

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Load balancing routing in RPL for the internet of things networks: a survey

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International Journal of Wireless and Mobile Computing 2023年第3-4期24卷 243-257页

作者： Venugopal, Kala Basavaraju, T.G. Department of Computer Science and Engineering Government Engineering College Karnataka Hassan India

Presently, when the Internet of Things (IoT) makes virtually everything smart by improving every aspect of our life, continuous development in this area is imperative. As IoT deals with the Low-Power Lossy Networks (LLNs) with constrained resources, Routing in such constrained networks is considered an acute problem. The Internet engineering Task Force (IETF) has come out with a de facto routing protocol for IoT networks called the IPv6 Routing Protocol for Low-Power and Lossy networks (RPL). Unfortunately, though RPL is formulated with numerous salient characteristics, load balancing is a principal concern that is left unaddressed. Load balancing in IoT guarantees fair dissemination of traffic load amid the nodes in the network and affects the connectivity, stability, reliability and lifetime of LLNs. This paper elucidates the state-of-the-art load balancing routing protocols and issues of load balancing, aiding researchers to design efficient and reliable load balancing routing protocols for IoT networks. © 2023 Inderscience Enterprises Ltd.

关键词： Internet of things

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An Automated Python Script for data Cleaning and Labeling using Machine Learning Technique

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第6期47卷 219-232页

作者： Oladipupo, Matthew Abiola Obuzor, Princewill Chima Bamgbade, Babatunde Joseph Olagunju, Kazeem M. Adeniyi, Abidemi Emmanuel Ajagbe, Sunday Adeola Department of Data Science School of Science Engineering and Environment University of Salford United Kingdom Jericho GRA Ibadan Nigeria Department of Computer Engineering Ladoke Akintola University of Technology LAUTECH Ogbomoso Nigeria Department of Computer Sciences Precious Cornerstone University Ibadan Nigeria Department of Computer & Industrial Production Engineering First Technical University Ibadan Nigeria

Every employee in the company who deals with data needs to have clean, noise-free data. Since data warehouses store and update enormous amounts of data from several sources, there is a potential that some of those references may contain inaccurate data. Due to the noise, inefficacy, and poor characterization of the vast amount of accessible data, as well as the ensuing insensitivity and inefficiencies of human data cleaning and labeling, the presentation of the data has become ambiguous, and the assessment of the information has become difficult. A hole in the creation of a better data analysis method was identified. This helped to guide the creation of a Python script for automatically cleaning and labeling data. The first step in the strategy used in this study to accomplish its goals and objectives was to obtain a financial dataset from the top database, "Kaggle". Create a machine learning (ML) approach in Python that intends to automate the financial dataset cleaning. This covers ingesting data, addressing incomplete data, addressing anomalies, one-hot wrapping and label encoding, extracting date and time values, and data normalization. Implementing an unsupervised machine learning method that attempts to automate financial dataset labeling (k-means). Using the method includes the elbow principle, k-means clustering, data modeling of "age" versus "arrival," dimensionality reductions, computer vision, and dataset categorizing using the groupings. An empirical assessment of the cleaned and labeled automated trading dataset utilizing a comparison of the cleaned dataset before and after PCA adoption. The results show that the developed ML technique not only improved the performance of the audit data used in this study, but also classified the data after cleaning it and removing the unpleasant section and incomplete data, as shown by the k-means segmentation result and grouping by PCA. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Python

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Improper Implementation of Transport Layer Security: Impact on Android Applications and Man-in-the-Middle Attack 2

Improper Implementation of Transport Layer Security: Impact ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Moon, Iffath Tanjim Mimi, Afsana Rahman Mridha, Md. Musfiqur Pundra University of Science & Technology Department of Computer Science & Engineering Bogura Bangladesh Varendra University Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

In this paper, we have analyzed the impact of improper implementation of Transport Layer Security (TLS) in Android applications. Communication security in Android applications is largely dependent on Transport Layer Security. For instance, data encryption, data integrity, authentication, trust-building, network attack prevention, and API and web service security are all provided by it. TLS preserves confidence and aids in preventing the unwanted exposure of sensitive data. However, improper implementation of TLS can pose a serious threat to the cybersecurity of an application. Therefore, software developers should ensure the security of applications, foster client confidence, and adhere to legal standards by skillfully implementing TLS. In this paper, we have chosen 25 miscellaneous Android applications and performed static analysis on them. Furthermore, we have identified various improper implementations of TLS in these applications. The riskiest TLS implementations in these applications include insufficient verification of the TLS certificate chain, invalid TLS certificates, insufficient checking of the TLS certificate validity, and insecure WebViews usage. For these vulnerable implementations of TLS, some applications are prone to a man-in-the-middle (MitM) attack. At last, we have provided the best practices of TLS implementations for Android applications. In addition, we have provided remediation for the vulnerabilities so that applications can be protected against MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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GSKTM: efficient of query search for spatial keyword in text mining

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International Journal of Information and Communication Technology 2024年第4期25卷 352-379页

作者： Reddy, Ramya Rayacherlu Sambasadasiva Manu, Darshan Naveen Raju, G. Nimbhorkar, Sejal Santosh Rajuk, Venugopal Kuppanna Iyengar, S.S. Patnaik, L.M. Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University MiamiFL United States INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

In today’s world, geo-positioning technologies, location-based services have attracted many researcher’s due to the increasing amount of spatio textual objects in various applications like social networks, geo location services. Each spatial object consists of spatial locations and a set of query terms. In this paper, an efficient group of query search for spatial keyword in text mining is proposed that retrieves both spatial and textual keyword objects to effectively reduce the search space. The clusters and subclusters are constructed based on the calculated range of the objects location and categories in the dataset. Further, categorylist is constructed that identifies the category of interest (CoI) of users query. Experiments are conducted on two real dataset namely Euro and geographic names. It is observed that GSKTM outperforms inverted linear quad-tree (ILQ) with improved response time and provides groupwise top-k results. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： group keyword query search spatial text feature selection

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