International Journal of Innovations in Science & Technology
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An Automated Approach for Enhancing Efficiency and Transparency in Student Selection Process for Public Sector General Universities
Get PDFThe admission process in public sector universities in Pakistan faces challenges, including a large volume of applications, complex eligibility criteria, and the need for equitable seat allocation across various quotas. Many public sector universities still rely on manual or semi-automated admission systems, which result in inefficiencies related to time and transparency. Furthermore, these systems are vulnerable to errors in the seat allocation process due to human involvement at certain stages.
To address these issues, this paper proposes a fully automated admission system for public sector general universities. The system is developed and implemented at the University of Sindh, Jamshoro, one of Pakistan’s oldest and largest public sector universities. Following the successful implementation of the system, a performance evaluation and comparative analysis are conducted to assess its effectiveness and confirm its feasibility for all public sector general universities in Pakistan.
Additionally, a usability study is carried out to ensure the system\u27s flexibility and ease of use from the user\u27s perspective. The results from the usability study and comparison indicate that the proposed system outperforms existing systems in terms of flexibility, reliability, efficiency, and transparency
A Comprehensive Study on Innovative AAC Solution for Enhancing Communication in Speech-Impaired Children
Get PDFAugmentative and Alternative Communication (AAC) systems serve as crucial communication tools for children who face speech and language difficulties. This paper outlines the design and development of an AAC mobile application specifically tailored to address the communication needs of children, allowing them to effectively express their thoughts and feelings. The app is created with the flexible Flutter framework and Visual Studio Code. Key features include symbol-based communication, text-to-speech, customizable symbols, and voice output, all suited to the specific requirements of speech-impaired youngsters. The user-friendly design emphasizes accessibility with vivid iconography for increased interaction. The software is evaluated in the paper using user satisfaction measures, real-world usage in educational contexts, and visual input from user interactions. A comparative analysis with existing AAC apps highlights the strengths of the proposed solution. The conclusion emphasizes the crucial role AAC apps play in aiding communication for children with hearing impairments. Future improvements include real-time capabilities, advanced feature extraction, and collaborative elements for user-caregiver communication, aiming to advance accessibility and efficacy in communication tools for this user group. This research contributes significantly to enhancing communication tools for children with speech impairments
Distributed Denial of Service (DDOS) Attacks Technique to Interruption the System\u27s Service and Identification
Get PDFDistributed Denial of Service (DDoS) attacks remain to present significant threats to network stability and security by flooding systеms with malicious traffic intеndеd to intеrrupt legitimate sеrvicеs. This dissertation looks at numеrous DDoS assault tactics and assеssеs thеir detection and mitigation using Snort and an opеn sourcе nеtwork intrusion detection system (NIDS). To adequately invеstigatе thеsе assaults and a thorough networks architecture was created and simulatеd with GNS3 and which includеd many VMwarе virtual machines to imitate a realistic network еnvironmеnt. Thе research investigates a variеty of DDoS attack tactics and such as volumetric assaults that flood the network with excessive data, protocol attacks that exploit vulnerabilities in network protocols, and software layer attacks that specifically target certain apps or services. The networks architecture gеnеratеd by GNS3 еnablеd thе controlled deployment of diffеrеnt attack vectors and offering insights on thеir influеncе on nеtwork performance and security. Snort was usеd to dеtеct and analyze thеsе assaults and taking usе of its rulе based detection capabilities to discover patterns and abnormalities associated with DDoS activity. Thе study assesses Snort\u27s еfficacy in detecting and rеacting to various DDoS attack signatures and with a focus on its rеal timе analysis of\u27 alerting systеms. Thе findings show Snort\u27s strеngths and limits in controlling various forms of DDoS assaults and offеring usеful insights into its rolе in improving nеtwork sеcurity. Furthermore, and thе study еmphasizеs thе nееd of a strong nеtwork architеcturе and ongoing monitoring in protecting against merging thrеats. Thе research presented hеrе contributes to our undеrstanding of DDoS attack dеtеction and thе actual implеmеntation of Snort in simulated network settings and including techniques for strengthening community resilience against attacks
Enhanced Skin Cancer Classification with MobileNetV3 and Morphological Preprocessing: A Deep Learning-Based Extension
Get PDFSkin cancer detection continues to pose challenges due to the visual similarity between the types of lesions and the limitations of traditional diagnostic methods. This study presents an extended and improved skin lesion classification framework that combines transfer learning with MobileNetV3 and enhanced preprocessing using mathematical morphological techniques. These preprocessing methods refine lesion boundaries and suppress irrelevant structures in dermoscopic images, thereby improving feature discrimination during training. The refined framework is evaluated using the ISIC dataset and achieves a notable classification accuracy of 89%, showing superior performance compared to baseline models. This extension also examines the generalizability and suitability of the model for deployment in low-resource mobile settings. The results validate the effectiveness of lightweight architectures paired with morphological enhancements, providing a reliable and scalable solution for early skin cancer screening and clinical support
Facial Recognition Attendance System
Get PDFFacial recognition technology is increasingly being used to enhance automation in various sectors like education. This paper presents the development of a class attendance system that leverages facial recognition to address limitations in traditional manual attendance methods, such as time consumption and susceptibility to proxy attendance.
This proposed system comprises four main stages: database creation, face detection, face recognition, and attendance updating. A database of student images is ready, after which Haar-Cascade classifiers and Local Binary Pattern Histogram (LBPH) algorithms are used for face detection and recognition in real-time classroom video streams. then The system automatically records attendance and forwards the data to faculty members at the end of each session
Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review
Get PDFMachine Learning (ML) can significantly enhance livestock management in various ways by providing real-time insights into animal health, behavior, and well-being. Livestock production, monitoring, and management can be revolutionized by using ML techniques. This study presents a comprehensive review of the literature regarding IoT devices used for monitoring cattle health, key characteristics of these devices, wearable technology used, sensors, and ML algorithms. In order to complete the review, a thorough examination and synthesis of the research articles published in reputable research venues between 2018 and 2023 are conducted. The findings revealed that pressure and pulse-rate sensors are the most often utilized types for recording the health status of animals experiencing health issues
Smart Home Monitoring System for Early Childhood Using Computer Vision Technology
Get PDFOne of the most significant problems families face today is the proper handling of newborns; most parents can barely always keep a close eye on their babies. Baby monitors put the minds of many parents at ease by increasing the safety of their children; however, many currently available models lack certain features that should comply with safety regulations. This paper proposes an intelligent monitoring system for infants that can be integrated into smart homes to improve real-time monitoring through a computer vision technique. Therefore, the primary goal of a smart home presence detection system is to enhance children\u27s safety by accurately identifying their presence and identifying risks that may arise in real-life scenarios. It operates in real-time to ensure parents are always informed of their child\u27s safety. This approach employs YOLOv5, which is well-known, fast, and accurate, thus suitable for this task due to its impressive real-time object detection performance. The proposed system indicates a quick and efficient framework for keeping children secure in smart homes, presenting the potential of advanced computer vision techniques in the real world
Seismic Data Analysis and Earthquake Prediction with IoT Sensors and SmartGRU Model
Get PDFTectonic plate movement causes a slow accumulation of stress in the Earth’s lithosphere, especially around plate borders, leading to earthquakes. An earthquake occurs when this stress overcomes friction along a fault or exceeds the strength of the surrounding rock. Accurate earthquake prediction remains challenging due to the complexity of seismic data and the limitations of traditional methods. This creates a pressing need for models capable of real-time analysis and high prediction accuracy. The Internet of Things (IoT) provides a novel method for detecting earthquakes using a variety of sensors to collect vital seismic data, such as latitude, longitude, depth, magnitude, and time. IoT controllers and centralized systems process and analyze this data to enable efficient monitoring and forecasting. Furthermore, with the help of a machine learning model named Bidirectional Gated Recurrent Unit (Bi-GRU), which integrates sophisticated data fusion and advanced machine learning techniques. Our proposed study model, SmartGRU, demonstrates how to improve earthquake prediction systems by combining IoT sensors with a Bi-GRU machine learning model that incorporates an emerging approach
AI-Driven Parking Management: ANPR-Based Entry & Biometric Gate Control
Get PDFThere is now a greater need for effective and safe parking solutions due to the growth in urbanization. To provide an anodyne parking experience, this article introduces an AI-driven parking management system that combines biometric authentication for gate control with Automatic Number Plate Recognition (ANPR) for vehicle classification. This paper will present an IoT-based automatic number plate recognition (ANPR) and biometric gate control system designed to optimize parking management through automated vehicle access. We suggested a biometric-integrated Internet of Things-based parking access management system with fingerprint recognition for user authentication. The system uses a Raspberry Pi 4 as its central controller and uses automatic number plate recognition (ANPR) to classify vehicles. Our suggested framework will utilize the camera to capture images of vehicles, then extract the license plate number and compare it to a database of permitted vehicles using ANPR software for vehicle classification and allocation. The system uses AI and IoT-based technologies to enhance security, automate vehicle entrances, and track real-time parking occupancy. Only registered users or authorized personnel are permitted to enter the restricted parking area. The proposed system is designed to operate in real-time, minimizing unauthorized access, reducing congestion, and enhancing overall parking efficiency. As a result of integrating with IoT systems, the solution will improve security and operational efficiency by enabling real-time monitoring, dynamic updates of parking availability, and logging of entry and exit events
Mutual Coupling Reduction in 5G Multiple Input Multiple Output Microstrip Patch Antenna
Get PDFThis study delineates the design and performance assessment of a small 28 GHz single-band Multiple-Input Multiple-Output (MIMO) antenna designed for fifth-generation (5G) wireless communication systems. The proposed antenna employs T-shaped gaps among radiating elements to mitigate mutual coupling, a critical issue in compact MIMO systems. Simulation results demonstrate a significant increase in isolation, with the Return Loss (RL) improved from −17 dB to −46 dB. Furthermore, the overall radiation efficiency increases from 71.5% to 76.8%, indicating an improvement in system performance. The design incorporates polarisation variety to alleviate multipath fading, a common challenge at millimeter-wave frequencies. The proposed antenna, characterized by its exceptional isolation, improved gain, and compact design, is well suited for integration into modern mobile devices and 5G-enabled platforms, including Internet of Things (IoT) networks, autonomous systems, and densely populated urban communication environments