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Intelligent surveillance support system

Discover Internet of Things 2023年第1期3卷 9页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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An Intelligent System to Predict Diabetic Retinal Diseases Based on Diabetic Attributes

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SN computer Science 2024年第6期5卷 755页

作者： Eswari, M. Shanmuga Balamurali, S. Ramkumar, K. Department of Computer Applications Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Rajalakshmi Institute of Technology

The most common illness among individuals and the general population in the medical field is diabetes. This is coupled with a careful diabetic retinal that has no signal. The historical record offers unrecoverable insight. To avoid a hallucinatory dispute at the time, early detection of retinal illness is necessary. Positively, the results of diabetic patients have been able to foretell the threat of diabetic-related eyeailments such as Diabetic Retinopathy, Glaucoma, and Diabetic Macular Edema.A diabetic retinal disease prediction exemplary have been created by via system learning rules, such as narrow neural network, bilayered neural network, wide neural network and trilayer neural network, in close proximity to the onset of the disease. The trilayer neural network with various activation functions accuracy based results servedas the foundation for the final model that evaluates real time data to predict the likelihood of *** learner representation accuracy of 0.95and the evaluation phase accuracy of 0.94 are *** ideal diabetic features-based diabetic retinal system outcome occupies a crucial place in the medical decision support system to make prompt assessments in the field of E-healthcare system to connect ophthalmology field to eradicate insulin based eye problems at the time of regular assessment. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Boosting computer-aided Design Diabetic Mellitus Ensemble Machine Learner Retinal eye Disease

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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Analyzing functional, technical and bibliometric trends of blockchain applications in education: A systematic review

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Multimedia Tools and Applications 2025年第8期84卷 4003-4048页

作者： Choudhary, Ankita Chawla, Meenu Tiwari, Namita Department of Computer Science and Engineering Maulana Azad National Institute of Technology Madhya Pradesh Bhopal462003 India

In an educational ecosystem, accurate and complete management of student’s records is a complex and error-prone process. Blockchain is a promising solution to add substantial value around enhanced efficiency, privacy protection, technological advancement, access control, and secure exchange of student course records. This work presents a systematic literature review to analyze the applications of blockchain technology in the educational domain addressing the gaps identified in the previous reviews. The technical papers published in English were searched by key terms related to blockchain and education on IEEE Xplore, Science Direct, and six other databases. The risk of bias was assessed by considering all the crucial search terms preventing the accidental exclusion of any article. Many blockchain frameworks in the educational domain have been proposed;including some prototypes and implementations. The PRISMA framework was used for the evaluation of 150 distinct models, 124 of which were prototypes, pilots, or full implementation and the remaining 26 were only proposals. This work contributes a structured bibliometric, functional, and technical examination of these articles, assessing privacy, cost, performance, security, architecture, and standards to identify and bridge gaps. The study concludes an avalanche of proposals, with approximately half of them being prototypes, indicating that implementation and proof-of-concept pilots are expected. Blockchain is found to solve various record management issues at significant trade-offs between performance, storage, and cost. However, scalability issues, poor overall performance, and increased price are major limitations for implementing blockchain in education solutions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Records management

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Navigation With Augmented Reality 1

Navigation With Augmented Reality

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Pawar, Urmila S. Kulkarni, Rutuja A. SCTR's Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

Augmented Reality (AR) and Virtual Reality (VR) are transforming user interactions, computation, market strategies, and businesses at an unprecedented pace. While VR immerses users in entirely virtual environments, AR overlays digital objects onto the real world, visible on devices such as smartphones or tablets. Mixed Reality (MR) integrates AR and VR, enabling interaction between the physical and virtual environments. AR's potential spans diverse fields, from gaming to education, enhancing our perception and interaction with the world. This paper presents a novel AR-based 'Navigation System' designed to provide intuitive and immersive guidance in unfamiliar environments. It aims to explore the feasibility and effectiveness of AR technology for navigation purposes, focusing on enhancing user engagement and usability. © 2025 IEEE.

关键词： Virtual environments

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Multi-channel mantis search spatial attention convolutional neural network based hyperspectral image change detection

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Multimedia Tools and Applications 2025年 1-22页

作者： Divya, V.M. Department of Computer Science and Engineering East West Institute of Technology Visvesvaraya Technological University Belagavi560018 India

Hyperspectral imaging has transformed remote sensing by offering detailed spectral data for monitoring environmental changes, like urban development and terrestrial cover variations. However, existing change detection techniques often face challenges like underfitting and overfitting. In this research, a novel approach is introduced for hyperspectral image change detection using a Multi-Channel Mantis Search (MCMS). This innovative methodology leverages the power of a Multi-Channel Convolutional Neural Network (MCNN) to process each spectral band independently, facilitating the capture of unique features within each band. The input hyperspectral images are gathered from the Pavia University Dataset and the Indian Pines Dataset. This approach leads to significantly improved feature extraction, enhancing the discrimination of different materials in hyperspectral images. To further enhance the performance of the change detection model, the Mantis Search Algorithm (MSA) is integrated. The MSA is employed to optimize the model's hyperparameters, fine-tuning its architecture and training parameters to achieve superior results. The combination of MCNN and MSA improves hyperspectral image change detection accuracy, achieving 98.21% and 99.31% with the Indian Pines and Pavia University datasets correspondingly. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Advancing automotive high-performance computing: integrating direction API with LORA technology in unified machine vision for future smart cars

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Multimedia Tools and Applications 2025年第18期84卷 19315-19342页

作者： Chaudhary, Kapil Department of Computer Science & Engineering BipinTripathi Kumaon Institute of Technology Gaucher Dwarahat Road Uttarakhand263653 India

This manuscript addresses the challenges in developing Automotive High-Performance Computing (HPC) systems for Future Smart Cars and Unmanned Mobile Vehicles (UMVs). We introduce a novel Unified Machine Vision (UMV) narrative methodology for operators overseeing UMVs. The integration of diverse sensors, Direction API software, and LoRa communication technology presents significant technical hurdles. To tackle these, we focus on integrating Direction API with LoRa in UMVs, incorporating open-source natural interaction OpenNI, and haptic driver inputs into the Master Control System (MCS). The MCS, using the Comp-GAP algorithm, serves as the central interface between the driver and machinery sensors, effectively managing commands to achieve multifaceted objectives. Additionally, we present the CCHA, CROMM-MSV parallel algorithm to mitigate power consumption issues in autonomous vehicles via centralized HPC. This new centralized Advanced Driver Assistance System (ADAS) methodology optimizes UMV energy consumption in air and enhances operational efficiency through parallel HPC techniques. Simulation results demonstrate stable behavior in parallel processing, validating the proposed methodologies. In summary, this manuscript offers a robust framework for HPC systems in Future Smart Cars and UMVs, paving the way for improved navigation and vehicular control. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： computer vision

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Bridging the gap in biomedical information retrieval: Harnessing machine learning for enhanced search results and query semantics

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9491-9510页

作者： Madhubala, P. Ghanimi, Hayder M.A. Sengan, Sudhakar Abhishek, Kumar Department of Computer Science and Engineering Bharathiyar Institute of Engineering for Women Tamil Nadu Salem India Information Technology Department College of Science University of Warith Al-Anbiyaa Karbala Iraq Computer Science Department College of Computer Science and Information Technology University of Kerbala Karbala Iraq Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Mathematics VIT Bhopal Bhopal India

The medical domain faces unique challenges in Information Retrieval (IR) due to the complexity of medical language and terminology discrepancies between user queries and documents. While traditional Keyword-Based Methods (KBM) have limitations, the integration of semantic knowledge bases and concept mapping techniques enhances data organization and retrieval. Addressing the growing demands in the biomedical field, a novel medical Information Retrieval System (IRS) is proposed that employs Deep Learning (DL) and KBM. This system comprises five core steps: pre-processing of texts, document indexing using DL (ELMo) and KBM, advanced query processing, a BiLSTM-based retrieval network for contextual representation, and a KR-R re-ranking algorithm to refine document relevance. The purpose of the system is to give users improved biomedical search results through the integration of all of these techniques into a method that takes into consideration the semantic problems of medical records. An in-depth examination of the TREC-PM track samples from 2017 to 2019 observed an impressive leading MRR score of 0.605 in 2017 and a best-in-class rPrec score of 0.350 in 2019, proving how well able the system is to detect and rank relevant medical records accurately. © 2024 – IOS Press. All rights reserved.

关键词： Information retrieval

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Unique hash value based identical video sharing traffic flow optimization across social networks

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Multimedia Tools and Applications 2025年第13期84卷 11983-12011页

作者： Konyak, Chenlep Yakha Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima Dimapur797 103 India

Identical video sharing across social networks results in redundant video data traffic, consuming additional bandwidth, storage resources, and data usage. A novel method is proposed to optimise redundant video-sharing services across social media applications exclusively on the push model, which is based on creating a unique hash value identity (UHVID) to identify the video distinctly. The UHVID is generated based on the centroid of gradient orientation of the key frames. The key frames are extracted from the video based on colour histogram and structural differences. Using UHVID and video metadata, the server application validates and optimizes identical video content flow before actual video sharing. The end users’ privacy is not affected in the proposed framework, as the actual video is not used for comparison, only the hash values of the video. The statistical analysis has shown that in 14 test cases of 2700 traffic initiations and 39,180 transactions, 83.2% of redundant data of identical videos were optimised, resulting in 63.61% data savings, amounting to 19.75 GB. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital storage

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Securing Next-Generation Quantum IoT Applications using Quantum Key Distribution

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IEEE Internet of Things Magazine 2025年第1期8卷 50-56页

作者： Bera, Basudeb Das, Ashok Kumar Sikdar, Biplab The Department of Electrical and Computer Engineering National University of Singapore Singapore The International Institute of Information Technology Hyderabad India

The next generation of Quantum Internet of Things (QIoT) has the potential to revolutionize various sectors, including smart homes, healthcare, and smart cities, by enabling more sophisticated and interconnected systems. These applications incorporate advanced features, such as autonomous decision-making based on Quantum Artificial Intelligence and Machine Learning (QAI/ML) and context-aware functionality. The security of the data in these applications relies on traditional cryptographic techniques, which, however, face a growing threat due to the significant advancements in quantum computing, especially with the Shor’s algorithm. This algorithm poses a substantial risk of breaking conventional cryptographic methods within a feasible timeframe. To address these emerging security challenges in the quantum realm, we propose a Quantum Key Distribution (QKD) based on the BB84 protocol. This approach aims to provide robust authentication using certificates through a classical channel and secure quantum key exchange via a quantum channel in a public environment. The proposed QKD scheme is implemented using a client-server model in Python and Qiskit, demonstrating its practicality in real-world applications. The obtained results showcase the successful establishment of a secure session key between IoT smart (sensor) devices and gateway nodes, effectively mitigating potential threats such as eavesdropping. © 2018 IEEE.

关键词： Smart homes

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