Volume 13 | Issue 7 | July-2025

 Abstract

Car parking is today a source of concern in city centers, and with escalating needs alongside a split, inadequate car park provision, one major negative outcome is the fact that road traffic becomes rather hard to handle, supported by the real congestion issues. They can result in inefficiencies as, for instance, massive levels of time wastage, plus greater consumption of fuel. Smart parking systems (SPS) can be described in terms of what they involve in offering innovative solutions through the application of contemporary technologies like sensors, Internet of Things (IoT) devices, and real-time data analytics to maximize the use of parking spaces. They are capable of providing pertinent, real-time information about parking availability to guide drivers into available spaces and enable payment processes. Furthermore, some intelligent parking systems incorporate dynamic pricing schemes, which calculate varying parking prices based on demand, hence reducing imbalances in occupancy levels among parking. This paper gives a general overview of intelligent parking systems, particularly their technological aspects and functionalities, and their impact on urban mobility. In addition, it emphasizes the advantages of the SPS such as traffic congestion reduction, environmental effects, and improvement in user experience. The uptake of smart parking systems is transforming urban parking management at a fast pace with efficient and sustainable strategies for congested cities.

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 Keywords

Smart Parking, IoT, AI, Real-Time Parking, Urban Mobility, Traffic Reduction, Autonomous Vehicles, Smart Cities, WSN

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 Abstract

Traditional cryptographic techniques, especially RSA and ECC, which are frequently employed in secure email systems, are seriously threatened by the quick development of quantum computing. We propose a Post-Quantum Secure Email System to solve this problem by including lattice-based cryptography, an encryption method that is impervious to quantum attacks. This system offers secure authentication, end-to-end encryption, and an effective structure for key management. By implementing NTRU and Kyber, secure email storage and retrieval is ensured, by reducing quantum vulnerabilities. A MongoDB-based storage solution combined with a Flask API enables real-time encrypted email processing. Performance evaluations show the system's storage overhead, encryption speed, and computing efficiency compared to traditional cryptographic methods. Its resistance to man-in-the-middle, brute-force, and quantum-enabled decryption assaults is validated by security analysis. In the quantum era, the proposed approach guarantees email secrecy and integrity over the long period.

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 Keywords

Performance tests measured encryption and decryption times, database query speeds, and system responsiveness under concurrent user loads, ensuring efficiency despite the higher processing cost of post-quantum cryptography. The system maintained an average email retrieval time of 50-70 ms, balancing security and usability.

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 Abstract

Video summarization is a fundamental task of computer vision and multimedia processing to condense lengthy videos into short representations without losing the valuable content and context. The study is founded on the use of object detection techniques in video summarization and relies on the capability of deep learning to automatically recognize and extract discriminative objects and events from video streams. Relying on the benefit of the latest object detection models and new summarization techniques, the study tries to enhance the efficiency and effectiveness of video summarization to allow users to quickly perceive the content and meaning of videos without the requirement of lengthy playback. The approach not only enhances video browsing and comprehension of content but also has its future areas of application in surveillance, video indexing, and content recommendation systems. Video summarization is a crucial element to successfully extract key moments from lengthy video recordings, reducing storage and processing costs, and enhancing the user experience. The work proposes a state-of-the-art video summarization technique founded on real-time object detection based on YOLOv3 and Deep SORT algorithms. Based on the fusion of the new approaches, the proposed method effectively extracts and tracks discriminative objects with improved efficiency, leading to informative and meaningful video summaries. Experimental results exhibit enhanced efficiency and accuracy compared to state-of-the-art summarization techniques, indicating the potentiality of the proposed methodology in its real-world applications like surveillance, sport analysis, and content generation.

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 Keywords

Video Summarization, Object Detection, Object Tracking, YOLOv3, Deep SORT, Real-Time Processing

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 Abstract

The deployment of 5G networks introduces a new era of communication technologies, provides higher data speeds, reduction in latency and enhanced connectivity. However, these advancements also increase significant concerns regarding security, especially with the increased attack surface and complexity inherent in 5G's architecture. Intrusion Detection and Prevention Systems (IDPS) are essential for safeguarding 5G networks against malicious threats and ensuring the integrity and availability of services. This paper surveys the state-of-the-art techniques for detecting and preventing in 5G environments, including both traditional and modern approaches. We discuss the role of machine learning and artificial intelligence in enhancing the detection capabilities of IDPS, as well as the challenges posed by the dynamic, distributed nature of 5G networks. Additionally, we explore the integration of IDPS with emerging 5G technologies such as network slicing, edge computing, and the Internet of Things (IoT), highlighting the potential for more adaptive and scalable security solutions. The paper also reviews key issues like real-time processing, scalability, and the need for privacy-preserving methods in intrusion detection. Finally, we identify research gaps and propose directions for future work to enhance the resilience of 5G networks.

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 Keywords

5G Network, Intrusion Detection and Prevention Systems (IDPS), Network Security, Cybersecurity in 5G, Machine Learning (ML)

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 Abstract

AgroPredict operates as a platform focused on vital agricultural issues such as crop selection and nutrient adjustment and disease prevention that specifically benefits Indian agricultural industries. AgroPredict delivers recommendations for appropriate crop choice through its IoT sensor applications by analyzing authentic soil data with environmental conditions of specific regions. Plant diseases become detectable through image recognition systems which allow early disease identification then the same technology analyzes symptoms while inspecting soil nutrient level to determine proper fertilizers based on plant requirements. AgroPredict achieves accurate predictions through the combination of three ML algorithms that include Random Forest with Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). Users can easily access the system using web and mobile interfaces because the system features intuitive functional design. Through data-driven information delivery AgroPredict helps farmers decrease financial losses and increase yields and adopts sustainable farming practices.

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 Keywords

artificial intelligence, crop prediction, fertilizer recommendation, IoT, machine learning, smart farming

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The proposed loan application processing .system for rural areas is specifically designed to address the unique challenges faced by agricultural communities when seeking financial assistance. This system aims to overcome the obstacles inherent to rural settings, ensuring a seamless and effective process for securing crucial financial support. Tailored to the specific needs of rural users, the system commences with farmers initiating the application process through a user-friendly interface designed explicitly for their use. A paramount focus is placed on robust data storage and management, ensuring the secure preservation of loan application forms. Employing advanced missing data imputation techniques enhances the integrity of the datasets. The website design emphasizes user interfaces that are both intuitive and accessible, accommodating varying levels of technological literacy prevalent in rural settings. The assessment of loan eligibility is facilitated by the integration of a machine learning model, carefully considering factors pertinent to agricultural finance. deployed locally and integrated via APIs, ensuring adaptability to both local systems and external services. The workflow concludes with a transparent and streamlined loan approval or rejection process, accompanied by insightful financial recommendations for approved applicants. This holistic approach, merging technology, effective data management, and machine learning customized for rural contexts, aspires to diminish the financial inclusion gap in rural areas. Ultimately, the system endeavors to empower farmers, enabling them to secure essential financial resources for sustainable agricultural practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Loan, Data, Validation.

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Creative Commons Attribution 4.0 and The Open Definition

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This study highlights the importance of cybersecurity and ethical hacking in India by examining the various policies, tools, and methods. The focus revolves around the efficacy of legal structures and policies regarding the cybercrimes that are surfacing in India. Cybersecurity is essential in today's digital world, as it shields private information, sensitive data, and vital infrastructure from online attacks. The swift development of digital technologies in India has resulted in a rise in cybersecurity risks, jeopardizing the availability, confidentiality, and integrity of critical data. Ethical hacking has emerged as a crucial tool in identifying and mitigating these threats. Strong cybersecurity measures aid in preventing monetary, reputational, and even loss of life due to the increase in cyberattacks such as ransomware, malware, and phishing. This report highlights the security concerns and difficulties by examining the present status of cybersecurity regulations and ethical hacking techniques in India. Strong cybersecurity regulations, practical ethical hacking techniques, and awareness campaigns are necessary to counteract cyber threats, according to a thorough review of the literature and professional viewpoints.

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 Keywords

cybersecurity, cybercrime, ethical hacking, cybercrime in India, government policies on cybersecurity in India, data protection laws in India, cybercrimes in India

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture is the primary source of food for the global population. It provides essential nutrients and sustenance for billions of people worldwide. Without agriculture, there would be no reliable food supply, leading to hunger and malnutrition. Agriculture plays a significant role in the economy, source of Raw Materials, Biodiversity and Ecosystem Services etc. This paper explores the Real-World uses of Machine Learning (ML) in sustainable agriculture to confront the difficulties of a growing global population and climate change. With traditional methods suffering from reduced effectiveness due to changing climate patterns and rising food demands, ML offers a data-centric approach to revolutionize crop management. The study investigates ML algorithms such as neural networks, support vector machines, decision trees, and ensemble models to analyze key factors like soil quality, climate conditions, and past crop performance. The aim is to optimize crop selection for specific regions, resulting in higher yields and reduced environmental impact.

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 Keywords

Smart Farming, Crop Yield Prediction, Soil Fertility Assessment, Machine Learning, AI Applications, IoT Applications

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper aims to develop an advanced system that detects and maps cyberbullying incidents on social media using AI technologies. The system will leverage Transformer techniques to identify relevant patterns and contextual signals from social media posts, accurately spotting instances of cyberbullying. Teachers can use the system to identify and support students affected by online harassment. Policymakers can benefit from precise data on cyberbullying trends and locations, enabling informed decision-making and the development of robust policies to combat online abuse. Social media platforms can utilize this technology to monitor and mitigate cyberbullying incidents, ensuring a safer digital environment for users.

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 Keywords

Cyber bullying, BERT, FSSDL-CBDC, Deep learning, Machine learning, Transfer learning, CNN

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 Abstract

Colonoscopy is essential for detecting colorectal cancer (CRC) and pre-cancerous polyps, allowing for timely intervention and better patient care. Nonetheless, the manual analysis of colonoscopy images can be slow and prone to human mistakes, which increases the likelihood of overlooking polyps or making incorrect diagnoses. This study examines the use of deep learning techniques to automate the detection and classification of polyps in colonoscopy images. By employing convolutional neural networks (CNNs) and sophisticated image processing methods, the research seeks to improve the accuracy, efficiency, and dependability of colonoscopy analysis, aiding healthcare providers in diagnosing conditions related to the colon. The focus of this work is on preparing colonoscopy images, isolating significant regions, and extracting important features to train a deep learning model for classification purposes. The suggested system framework combines the segmentation and classification models to differentiate between normal and abnormal colon tissues. The method has been evaluated using a thorough dataset of colonoscopy images, showing significant enhancements in detection accuracy compared to traditional methods.

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 Keywords

A DIAGNOSIS OF COLON CANCER USING DEEP LEARNING ALGORITHM

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