Frontiers in Engineering and Informatics
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Smart Surveillance: Multi Weapon Armed Person Detection With Automated Threat Alerts
The rise in criminal activities has created an urgent demand for intelligent surveillance and command systems in law enforcement. This work introduces a deep learning–based model tailored for classifying distinct weapon categories. The model is developed on the YoloV8-CNN+LSTM framework and implemented using Keras with TensorFlow as the backend. It is trained to recognize gun, rifles, knives. A carefully curated dataset containing 5,214 images was prepared for training and evaluation. The proposed network underwent comparative testing against established models, including VGG-16, ResNet-50, and ResNet-101. Results show that the model achieved an improved accuracy of 92\%, outperforming VGG-16 (89.75\%), ResNet-50 (90.70\%), and ResNet-101 (83.33\%). These findings helps to improve proposed approach in enhancing weapon detection accuracy, thereby strengthening the capabilities of security forces in preventing crimes.
Quantum Computing: A Threat to Blockchain Technology and Potential Solutions
This article explores the potential implications of quantum computing in the field of blockchain technology. The inherent security and immutability of blockchain technology may be compromised by the future ability of quantum computers to undermine the cryptographic methods upon which its security is built. This article examines the potential impact of quantum computing on blockchain technology, specifically focusing on the vulnerabilities that may arise from the utilization of Grover’s algorithm, which might potentially undermine the validity and integrity of the blockchain. Furthermore, the essay delves into the ongoing research and development endeavors in the fields of blockchain and quantum computing. It also examines prospective remedies to address the risks associated with quantum computing, such as the use of cryptographic algorithms that are resistant to quantum attacks. The conclusion underscores the significance of remaining abreast of the most recent advancements in this domain and adopting proactive strategies to mitigate the potential impact of quantum computing on blockchain technology.
Text Summarization Using NLP and Fuzzy Logic Based ML Techniques
In today world, there are large amount of content that are available digitally and extracting information from them become more challenging for today’s situation. However, automated summarization plays major role in extracting features from the data. Traditional summarization method have certain limitations such as lack of multiple language support. This paper proposes the advanced framework to summarize news text effectively using Natural Language Processing and Machine Learning based Techniques. With help of NLP, we can extract future insights and advanced features that help us understand the content more precisely. With help of fuzzy logic scoring, we can score each sentence effectively based on certain model and extract important sentence that are required for Summarization. As said earlier, ML based scoring also include evaluating certain concepts like term frequency and inverse document frequency (TF-IDF) and similarity in semantics to evaluate the sentence. This current study combines ML based fuzzy scoring and advanced NLP techniques to summarize the news data.
MARLIN-NET: A DEEP LEARNING FRAMEWORK FOR UNDERWATER MARINE SPECIES RECOGNITION
This paper presents a deep learning structure for identifying underwater marine species and classification using an integrated feature fusion architecture that leverages multiple CNN architectures. It enhances underwater image recognition with the proposed system by fusing the features from MobileNetV2, EfficientNetB0, and InceptionV3. This collaborative way of extracting features captures both fine-grained textures and high-level spatial representations, dealing with several visual issues related to underwater turbidity, low illumination, and color distortion of pictures. The model is trained and tested using a balanced dataset of marine species. Generalization is improved with resizing, normalizing, and augmentation of images. Some performance metrics involve accuracy, F1-score, recall, and precision in classification.The results have shown significant elevations compared with individual CNN models and thus prove the benefits of feature fusion in complex underwater scenarios. We would like to further implement the developed framework using PyTorch on Google Colab to enable efficient computation and easy scalability. In general, the system contributes to ocean biodiversity research and ecological conservation, making real-time monitoring of marine life possible.
From Strategy to Execution: A Practical Roadmap for SME Cloud Migration
Due to the high rate of market growth, small and medium-sized businesses (SMEs) are increasingly under pressure to modernize their IT infrastructure In 2024 the worldwide spending on cloud is estimated to reach $351.8 billion, and in 2033 the figure will increase to $1,230.8 billion. Cloud solutions provide SMEs with an economy to pay-per-use, real-time scalability, enterprise-level security, and advanced services (AI/ML, analytics), allowing them to keep up with the changing market and stay competitive in the digital economy. Poor migration planning, insufficient in-house knowledge, unexpected expenses, and regulatory burdens, however, contribute to migrations gone awry. Where migration projects cost long-term, security lapses, and operations become out of control are a point of reality. To overcome such obstacles, we present a practitioner-driven seven-step Action Framework of SME Cloud Migration: Readiness Assessment; Policy Alignment & Strategy Design; Technology Selection; Security Architecture Planning; Migration Execution; Training & Change Management; and Continuous Monitoring & Optimization. Our framework is based on best practices across industries, as well as the operational case patterns, placing smaller businesses on the path to avoiding some of the most widespread cloud pitfalls, including cost-based lift-and-shift traps, vendor lock-in, and post-migration neglect, and to maintain the long-term value of cloud through the process of steady improvement.
Face Recognition Framework using Generative AI integrated with Maximum Entropy Regularized Decision Transformer and Crayfish Optimization Algorithm
Face recognition using generative Artificial Intelligence has emerged as a powerful technique for learning robust identity representations under varying illumination, pose, and expression. By leveraging data synthesis and latent space modeling, generative AI enhances generalization and improves recognition accuracy. However, conventional generative-based systems often face limitations such as overfitting, noise sensitivity, and insufficient optimization of deep features, leading to performance degradation in real-world scenarios. To overcome these challenges, this study presents a Face Recognition Framework using Generative AI, integrated with the Maximum Entropy Regularized Decision Transformer and Crayfish Optimization Algorithm (MERDT-CryfOA). Initially, facial data are collected from the Labeled Faces in the Wild (LFW) dataset. It undergoes pre-processing using Adjusted Min–Max with Decimal Scaling and Statistical Column Normalization (AMnMx-DS-SCN) to enhance feature uniformity and eliminate intensity bias. The processed data are then fed into an Improved ResNet-34 Algorithm (KANS-ResNet-34) model for feature extraction, capturing fine-grained and nonlinear facial attributes such as wrinkles, skin tone gradients, and contour transitions. These deep features are subsequently classified by the Entropy Regularized Decision Transformer (MERDT), which employs entropy-regularized policy learning to ensure adaptive, robust decision-making. Finally, the Crayfish Optimization Algorithm (CryfOA) is employed to optimize the classifier’s loss function, balancing exploration and exploitation to achieve faster convergence and reduced misclassification. Experimental results show that the proposed MERDT-CryfOA model achieves a recall of 99.4%, precision of 99.3%, FAR of 1.17%, and FRR of 1.09%, outperforming existing deep learning methods. These results confirm its superior accuracy and robustness under varying conditions, making it an effective framework for next-generation face recognition systems.
Exploring the Revolutionary Potential of Quantum Technologies in the Fintech Industry
Quantum computing plays a crucial role in enhancing risk management across several industries. In contrast to conventional linear algorithms employed in risk management, quantum computing possesses the capability to process extensive quantities of data and execute intricate computations at astonishing velocities. This facilitates managers in acquiring a more comprehensive comprehension of risk elements. Financial modeling is an area in which quantum computing can be effectively utilized for risk management purposes. Quantum computing can be employed by managers to monitor market patterns, assess investment opportunities, and forecast financial results. This can lead to the implementation of well-informed decision-making processes and a decrease in potential risks. Health risk management is a crucial aspect of the healthcare industry. Consequently, the attainment of improved patient outcomes and the reduction of healthcare expenditures can be realized. In general, quantum computing possesses the capacity to assist managers in the realm of risk management through its ability to facilitate more accurate and comprehensive data analysis, expedite the process of decision-making, and enable the simulation of intricate scenarios. This enables managers to proactively identify and address potential hazards, leading to improved corporate performance.
A Hybrid Deep Learning Approach for Accurate and Efficient Object Detection
Humans can easily identify multiple objects in an image or video but for computers, it is very difficult to identify. The procedure of precisely detecting and promptly recognizing items is quite challenging. However, with the assistance of diverse object detection algorithms, we can now do this work with utmost precision. The proposed method achieved an accuracy of 0.78 to 0.84 on various objects. The advantage of embedding mask RCNN and yolo v7 was achieved with a good precision value. The experimental results were published by masking the specific object and masking the background of the image. The Advantage of using Embedded with Mask R-CNN and YOLO v7 is that it achieves a good precision value. The experimental results were published with masking applied to a specific object and its background. The proposed method concluded that, with YOLO v7, we could reduce the computational effort by 30% and parameter optimization by 40% compared to the existing method.
Edge-Enabled Hybrid AI Framework for IoT-Based Crop Management and Irrigation Control
The intersection of the Internet of Things (IoT) and Artificial Intelligence (AI) technologies has ushered in a new era of modern agriculture, transforming it into a data-driven, intelligent, and sustainable system. This paper presents an IoT and AI-based Agriculture Framework (IAAF) that integrates heterogeneous sensor networks, drone-based imaging, and hybrid deep learning for real-time precision crop management. The IAAF implemented a CNN-LSTM network for time-dependent sensor data analysis, a transfer-learning-based CNN for multispectral image classification, and a multimodal attention-based fusion model that combines time-dependent and spatial information to facilitate integrative decision-making. Additionally, the framework utilized edge computing to minimize latency and reduce bandwidth consumption and cloud services to manage model retraining and facilitate long-term analysis. Experimental assessments using multi-season field datasets provide evidence that the IAAF framework achieved significantly better performance than baseline models, with a prediction accuracy of 98.78%, a precision of 98.43%, a recall of 98.51%, and an F1 score of 0.986. Accordingly, the IAAF proposes an innovative, intelligent, and resource-efficient model of real-time precision agriculture, promoting a sustainable vision of smart farming.
Performance Evaluation of a 5G-Powered Vehicle-to-Infrastructure (V2I) Framework for Intelligent Traffic Control
Urban mobility has become a major concern due to urban sprawl, increased traffic and difficulties in improving infrastructure. Congestion, increased carbon emissions and fuel waste result from the incapacity of current traffic management systems to react swiftly and effectively. To solve this issue, we propose creating an Intelligent Urban Traffic Management System (IUTMS) that connects vehicles and infrastructure to improve traffic flow. This system would combine 5G technology, Internet of Things (IoT) sensors, and cloud-based artificial intelligence (AI) analytics. The system incorporates a variety of Internet of Things devices, including computer vision cameras, LiDAR modules, and infrared sensors. These devices monitor vehicles and their surroundings. The devices transmit this data to a cloud-based service for processing and decision-making via low-latency 5G wireless communication. Our IUTMS system was tested in an 8x8-kilometre urban study area with 36 junctions, powered by Apache Kafka, ns-3 (5G-LENA) and SUMO. The proposed framework has an end-to-end latency of between 6.87 and 15.94 milliseconds. Even under truck-density stress, the service remains reliable, achieving a Packet Delivery Ratio (PDR) of 85.46% to 95.86%, a Network Throughput of 78.12% to 93.84% and latency of less than 10 ms. This work improves and validates the prediction and input aspects of the suggested framework, leading to a low-latency, high-reliability and efficient real-world system.
A Hybrid Quantum based Self-Variational Onsager Neural Network with Leech Growth Algorithm-based Clinical Decision Support System for Accurate Heart Disease Detection
Heart disease has been among the major causes of death in the world, and unless it is detected properly and on time, it cannot be treated effectively. Conventional methods of diagnosis generally have a problem with the complex clinical data, which results in misdiagnosis and delayed interventions, and that is why advanced computational methods are the ideal solution to this need. In this regard, the proposed research proposes the Hybrid Quantum based Self Variational Onsager Neural Network with Leech Growth Algorithm (HQ-SVONNet-LGA) to identify heart disease. At first, the Kaggle heart disease dataset is processed and prepared clinically according to the Z-score Min–Max Normalization (Z-M-MN) to provide consistency and reliability. HQ-SVONNet model represents intricate patterns in the data, whereas the Leech Growth Algorithm is an optimization of network parameters to achieve better convergence and accuracy. Experimental findings indicate that the proposed model works better than the existing approaches with a 99.1% accuracy rate, 98.7% precision, 99.5% specificity, and a low error rate of 0.9%. To summarize, HQ-SVONNet-LGA is a powerful, accurate, and effective model of reliable heart disease detection.
A Visual-Modified Circular Dilated Convolutional Neural Network with Draco Lizard Optimizer Framework for Automated Lung Disease Detection Using CT Images
Lung Disease is one of the prominent causes of death, hence the need for its early diagnosis and accurate detection via CT images. Conventional approaches to diagnosis may have limitations in identifying intricate patterns in lung disorders; hence, the possibility of diagnosis may suffer, leading to delayed medical attention. To mitigate these limitations, the research proposes the Visual-Modified Circular Dilated Convolutional Neural Network with Draco Lizard Optimizer (V-CDCNNet-DLO) for efficient classification of lung disorders. CT images from the LIDC-IDRI dataset are first preprocessed using the Adaptive Self-Guided Loop Filter (AS-GLF) to enhance image quality. The research utilizes the V-CDCNNet for feature extraction and classification, with the help of the weights provided by the Draco Lizard Optimizer (DLO) to improve its performance. The research has demonstrated excellent performance with a high level of accuracy of 99.27%, precision of 99.20%, recall value of 99.15%, and error level of 0.73%. This model establishes a reliable framework for early lung disease detection.
Visual-Modified Circular Dilated Convolutional Neural Network
A Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks with Dandelion Optimizer Model for Breast Cancer Detection Using Histopathological Image
Breast cancer is one of the common and life-threatening disorders in the world, as it requires proper and timely diagnosis to enhance the survival of the patient. Conventional diagnostic tools tend to be unable to evaluate high-dimensional histopathological image data, and the data is misclassified, which can lead to delayed treatment. To overcome this, the Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks with Dandelion Optimizer (QM-SS-HP-GNN-DO) is suggested to provide effective breast cancer diagnosis. To begin with, the images obtained and used in histopathology are gathered and preprocessed by Trainable Self-Guided Filtering (T-SGF) to improve the features of interest included in the BreakHis dataset. QM-SS-HP-GNN model subsequently provides classification, and Dandelion Optimizer (DO) optimizes the network weights to enhance the accuracy of the network. Experimental performance is better with an accuracy of 99.43%, precision of 99.10%, sensitivity of 99.05%, specificity of 99.20%, and F1-score of 99.08%, showing the best results compared to existing models. Overall, the suggested model offers a well-constructed, stable, and extremely precise framework of breast cancer diagnosis, and can be adapted to other medical image-based disease detection problems.
Quantum Mixed-State Self-Harnessing Physics-Guided Neural Networks
A Dual Attention Holographic Convolutional Neural Network with Perfumer Optimization Algorithm for Accurate Brain Tumor Detection Using MRI Images
The classification of Brain Tumor (BT) based on MRI images is one of the most crucial tasks in medical imaging because proper diagnosis directly influences the early treatment planning and patient survival. Although deep learning has made progress, it still experiences difficulties with accurate tumor segmentation, boundary preservation, and accurate differentiation of features across different tumor grades. In order to overcome these shortcomings, the proposed Dual Attention Holographic Convolutional Neural Network with Perfumer Optimization Algorithm (DAHCNNet-POA) offers a strong baseline of automated BT classification. The MRI images are first acquired on the BraTS 2018 dataset. Shape-Aware Mesh Normal Filtering (S-AMNF) is applied to perform preprocessing that removes noise without losing the structural and boundary integrity. A Visual Geometry Grounded Transformer (VGGT) is used to segment tumor regions accurately and capture both spatial and geometric relationships. After that, the discriminative features are obtained and classified in the DAHCNNet, which incorporates both spatial and channel attention to improve the learning of the representation. The network weights are optimized through POA, which enhances convergence and performance. The model got 99.12% accuracy of HGG, 99.18% accuracy of LGG, and Dice scores greater than 99, which proved its high reliability and effectiveness.
Dual Attention Holographic Convolutional Neural Network
A novel Capsule Dual-Channel Convolutional Block Attention Neural Network with Carpet Weaver Optimization-based intrusion detection system in IOT networks
The development of Internet of Things (IoT) networks has made them susceptible to cyber-attacks, and thus, proper Intrusion Detection (ID) is the key to safe communication. Current detection systems are usually characterized by low accuracy, high false alarms, and low capability to learn complex feature relations in heterogeneous IoT traffic, which shows the necessity of a more powerful solution. To tackle these issues, this paper proposed the Capsule Dual-Channel Convolutional Block Attention Neural Network with Carpet Weaver Optimization (CD-CCBANNet-CWO) in terms of detecting intrusions. The information gathered on the TON-IoT dataset is preprocessed with Pearson Correlation Coefficient and MinMax Normalization (PCC-MMN). The processed data is then input into the CD-CCBANNet model, and network weights are optimized using Carpet Weaver Optimization (CWO) to provide better convergence and accuracy. Experimental outcomes prove that the model has a 99.45% accuracy, high recall, precision, and F1-score, and a low error rate of 0.55, which is much better compared to the current methods. To sum up, CD-CCBANNet-CWO is a trustworthy and high-performance ID network.
A Novel Approach to Predict the Lungs Cancer Using the Hybrid Deep Learning Model
Lung cancer is a predominant source of cancer-related mortality globally, and enhancing survival rates necessitates early detection. This research offers an integrated model that combines thresholding-based segmentation with convolutional neural networks (CNN) to predict lung cancer from CT scan pictures accurately. The initial step to isolate lung nodules from the background entails preprocessing the pictures by thresholding to generate binary images. During the second phase, a CNN is employed to analyze the segmented nodules and categorize them as benign or malignant. Our suggested model demonstrates substantial enhancements in critical performance parameters, including sensitivity (96%), recall (93%), precision (95%), and accuracy (97%), when compared to other models such as FCN, U-Net, and U-Net++. The results indicate the model's proficiency in reliably identifying and classifying lung nodules, positioning it as a potentially valuable instrument for early lung cancer diagnosis in clinical environments.
Privacy-Preserving and Decentralized Authentication for IoV Using Federated Learning and Blockchain
The Internet of Vehicles (IoV) is a rapidly evolving field, and it is imperative to safeguard user privacy while ensuring effective and secure authentication. Conventional centralized authentication methods are susceptible to single points of failure, privacy issues, and data breaches. This study presents an innovative decentralized authentication method utilizing federated learning and blockchain technology to address these concerns. Federated learning safeguards private data by allowing vehicles to collectively develop authentication models locally, hence eliminating the need for data transfer to centralized servers. Blockchain offers a secure, immutable ledger for documenting authentication activities, enhancing transparency and minimizing manipulation. Our privacy-preserving methodology reduces communication overhead and computational expenses while providing real-time, scalable, and tamper-resistant authentication for IoV scenarios. The proposed technique outperforms conventional methods for computational expense, communication costs, throughput, and security robustness. This methodology integrates the advantages of federated learning and blockchain technology to provide a strong solution to the increasing security demands of IoV networks.
Efficient Medical Image Compression Using a Hybrid Deep Learning Framework: Integrating Transform Coding and Neural Architectures for Enhanced Diagnostic Integrity
Medical image compression is essential for maintaining diagnostic accuracy while reducing the storage and transmission requirements of medical image data. This study presents a hybrid deep learning model that integrates conventional transform coding methods, including wavelet and discrete cosine transform for frequency domain coding, autoencoders for dimensionality reduction, and convolutional neural networks or spatial feature extraction. The approach significantly reduces the dimensions of medical images and achieves high compression ratios while preserving essential diagnostic information. Performance analysis shows that the proposed model outperforms previous methods, achieving a compression rate of 68%, a peak signal-to-noise ratio (PSNR) of 46, and a structural similarity index (SSIM) of 0.91. The results indicate that the model achieves exceptional compression efficiency and image quality, making it suitable for practical medical imaging applications that require significant compression and image quality preservation.
IoT-Driven Air Quality Monitoring and Predictive Analytics Framework Using Machine Learning for Enhanced Indoor Environmental Health
Humans are susceptible to poor indoor air quality, but there are ways to prevent this. This study describes a method for monitoring and detecting indoor air quality to assess many parameters, including carbon dioxide, volatile organic compounds (VOCs), PM2.5, PM10 particulate matter, carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, temperature, and humidity. The device collects real-time air quality data using microcontrollers and advanced sensor technology. Consolidating all of this data onto a single platform facilitates continuous monitoring and enables rapid response. The primary goal is to maintain air quality standards within safe and optimal limits to improve occupant comfort and health. Preliminary testing in several confined environments has demonstrated its effectiveness in providing accurate and timely air quality information. This modular device will serve as a critical tool for achieving optimal indoor air quality due to its ease of installation and real-time monitoring capabilities. Predictive analytics will be integrated into future developments to facilitate proactive management of anticipated air quality issues.
Integrative Mobile Sensor Framework for Driver Behavior Analysis: Real-Time Monitoring of Facial and Physiological Parameters to Enhance Road Safety
Everywhere in the world, the behavior of drivers has been the various reasons for horrible accidents, which are causing deaths, fatalities, and injuries. Every Day, the number of accidents is increasing globally. Many scientists have concluded drivers have less sleep and more workload, which causes drivers' mood changes. In this paper, an experimental model is used to detect the drivers' behavior to decrease the possibility of accidents caused by this problem, which increases driver safety. During this work, two methods effectively detect a person's behavior. The Driver's face is captured, eye retina detection and facial feature extraction are completed, blinking values are calculated, and threshold values are set. Secondly, an Aurdino module is used, which is integrated with elastomeric sensors for real-time calculation of driver hand pressure on the wheel, and the threshold value is prepared. A result from both methods is taken as input for making a judicial decision and alerting the driver.
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