International Journal of Innovations in Science & Technology
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Flood Risk Assessment Using Geospatial Techniques: A Case Study of River Ravi-Punjab-Pakistan
Get PDFFlooding, an increasingly prevalent environmental hazard, has been worsened by climate change, particularly affecting developing countries. Pakistan is especially vulnerable to hydrological hazards. This study aims to evaluate flood risk using geospatial technology and analyze return periods to assess the impacts of floods on crops. Water is a significant driver of landscape change. Landsat 8 datasets are utilized to examine crop patterns and built-up areas. Return periods of 50, 100, and 250 years are used to define risk zones, with gauges at Jassar, Syphon, and Shahdara considered. Historical images from 1995, 1996, and a recent year are analyzed to track changes in crop and built-up areas. SRTM and Pulsar DEM data are employed to study the watershed. The analysis indicates that over a 150-year period, the probability of a significant flood event is 0.25. This low probability suggests minimal water flow, with only small amounts arriving during the monsoon season, causing minimal disruptions to crops. These probabilities are based on established methodologies. While the probability of a significant flood event over 250 years is very low, it is included for classification purposes. The chance of a flood affecting crop patterns is 0.5, but this may vary if river water levels rise due to other sources. Nonetheless, current data and historical records indicate that the likelihood of floods significantly impacting crop patterns remains very low
A Critical Evaluation for the Energy Efficient Routing Protocols in Wireless Body Area Sensor Networks (WBAN)
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Wireless Body Area Network (WBAN) is a promising technology for providing intelligent healthcare services in remote locations. A review of the literature shows that researchers have primarily focused on Quality of Service (QoS)-aware energy-efficient routing techniques, network topology, and Medium Access Control (MAC) layers in WBANs. QoS-aware routing techniques are based on a set of protocols that efficiently maintain routes and effectively facilitate data exchange between sensor nodes. This research introduces WBAN and discusses its medical applications in detail. It provides a classification of routing protocols in WBAN and addresses the challenges researchers face in QoS-aware routing. Additionally, a framework for an energy-efficient routing protocol in WBAN is developed, aimed at healthcare authorities for use in emergency rescue operations
A Dynamic Architecture to Control Multi-Rotors Using Hand Gestures
Get PDFTraditional methods for controlling multi-rotors typically involve joysticks, radio controllers, and mobile applications. However, these methods pose significant challenges, particularly for novice users like farmers, due to the extensive training and understanding required to effectively operate a copter. This paper introduces a highly adaptable architecture designed to offer an end-to-end solution for controlling a copter using hand gestures. The proposed system leverages a depth sensor and Convolutional Neural Network (CNN) to recognize hand gestures, utilizing a custom dataset collected from both indoor and outdoor environments. Through a series of simulations with novice users, the system has demonstrated successful operation in real-world scenarios. Currently, the architecture can accurately recognize six distinct gestures with an average accuracy of 90.5% across three different test environments with varying lighting conditions. Key features of this proposed solution include its adaptability, reliable performance, especially in low-light conditions, and its user-friendly design, making it particularly well-suited for farmers and other inexperienced users
A Review based on Active Research Areas in Mining Software Bug Repositories: Limitations and Possible Future Trends
Get PDFIntroduction/ Importance of Study: Bug repository mining is a crucial research area in software engineering, analyzing software change trends, defect prediction, and evolution. It involves developing methods and tools for mining repositories, providing essential data for bug management.
Objective: The goal of this study is to analyze and synthesize recent trends in mining software bug repositories, providing valuable insights for future research and practical bug management.
Novelty statement: Our research contributes novel insights into mining software repository techniques and approaches employed in specific tasks such as bug localization, triaging, and prediction, along with their limitations and possible future trends.
Material and Method: This study presents a comprehensive survey that categorizes and synthesizes the current research within this field. This categorization is derived from an in-depth review of studies conducted over the past fifteen years, from 2010 to 2024. The survey is organized around three key dimensions: the test systems employed in bug repositories, the methodologies commonly used in this area of research, and the prevailing trends shaping the field.
Results and Discussion: Our results highlight the significance of artificial intelligence and machine learning integration in bug repository mining; that has revolutionized software development process by enhancing classification, prediction and vulnerability detection of bugs.
Concluding Remarks: This survey aims to provide a clear and detailed understanding of the evolution of bug repository mining, offering valuable insights for ongoing advancement of software engineering
Predicting Depression Among Type 2 Diabetic Patients Using Federated Learning
Get PDFDepression being a common and dangerous mental health condition could have significant impact on a person\u27s quality of life. It may result in depressive and gloomy feelings along with a loss of interest in once-enjoyable activities. Depression is considered a leading global cause of impairment that affects people at various stages of age, ethnicities, and socioeconomic statuses. It may cause negative effects on person’s physical and emotional well- being like reduced motivation, energy, and appetite. In this paper, we have presented Federated Learning-based framework to predict depression in patients with type 2 diabetes. Type 2 diabetes frequently coexists with depression, which can have a negative impact on treatment outcomes and raise medical expenses. Objective of this paper is to create a Federated Learning- based framework to predict the impact of depression in causing type-II diabetes by analyzing patient’s data that include laboratory results, medical history, and demographic information. To forecast the likelihood of depression in patients with type 2 diabetes. Analysis has been performed using freely available dataset of Type-II diabetes from Kaggle and accuracy of 97% has been achieved
The Assessment of Public Participation Modalities through Social Media Platforms for Approval of Private Housing Schemes: Case Studies under LDA Lahore, Pakistan: A Case of Lahore, Pakistan
No full textPublic participation through social media networks in Private Housing Scheme (PHS) projects is essential for fostering a feeling of community and avoiding resistance to the planning of housing scheme initiatives. It might help the private developers and government in identifying potential hurdles to any given landuse, allowing officials to work to eliminate them before making a final decision. This study will look at public participation in private housing scheme projects through online platforms in the metropolitan corporation Lahore. It emphasizes how the Government and Lahore Development Authority (LDA) encourage residents to participate more actively in PHS projects and the requirement of aligning tools with goals to enhance citizen engagement. To get a comparative understanding, the approaches and practices of public engagement in urban planning projects in selected industrialized and developing nations and Pakistan have been critically studied. On the other hand, Social media plays effective role in engaging public in concerned projects. It allows for cost-effective, efficient information sharing among public/stakeholders through various media types, including videos. It allows for the education of a broad audience about issues and encourages engagement. It can be used alongside other communication initiatives for wider public/stakeholder interaction. Moreover, participant\u27s education was greatly aided by public consultation. It is maintained that public engagement in PHS is steadily increasing in Lahore, Pakistan despite some obstacles. Applying a more proactive strategy throughout the PHS clearance process and prior to site selection for development projects is one suggestion made to improve PHS public engagement effectiveness in Pakistan
Smart Tutor: Ensuring User Privacy in Distance Education with AI-Driven Tutoring and Multilingual Knowledge Retrieval
Get PDFToday, in this digital era, getting access to all such information is essential for education and learning. Indeed, most of the existing content is useful if utilized appropriately or in conjunction with developed solutions, nevertheless, people from developing areas may have a tough time understanding because the available information is not organized in one place or lacks clear and structured content. Smart Tutor is a web application built to address these challenges and we believe that information access should be updated by modern technology based on it. Smart Tutor — which is brainstormed as a knowledge assistant integrates with Google Gemini AI and Wikipedia API to deliver content on various topics in the most beautiful, feasible manner. It also includes real-time translation built on top of Google Translate, so the geographical areas and even language are not a barrier anymore. Smart Tutor also features text-to-speech in multiple accents across English and enables users to listen in their preferred accent as well as alternative visualized data for different learning types. Through centralization of scattered information, language accessibility improved and ensured safety, this promotes digital literacy enabling access also to information-disadvantaged people. The intuitive design and custom features of Smart Tutor offer an ideal resource for users from all walks of life who want to easily connect with available offers to help grow digital knowledge
Parametrical Analysis of Symmetrical Double U-Slots Micro Strip Circular Patch Antenna for Wireless Communication Devices
Get PDFThe advancement in telecommunication demands antennas having wide bandwidth, compact size, and high performance. On the other hand, the microstrip antenna has a narrow bandwidth and low radiation efficiency. Recently, a number of researches were made to improve the bandwidth of microstrip antenna. The proposed antenna is based on to studied parametrically the microstrip antenna and their effect has been analyzed in terms of return loss, radiation patterns, and current distribution on the surface of the patch. The parameters such as the radius of the patch, lengths and widths of the slots, and feed point location are changed to different values using CST Microwave Studio. In this article double U-slotted in circular patch microstrip antenna are designed. The circular patch of radius 18mm and thickness of 1mm, FR4 substrate of permittivity of 4.3, dimension of (40 X 50) mm2, the thickness of 3.6mm, and coaxial probe are used to design the proposed antenna. The designed antenna has resonance frequencies at 3.2GHz, 5.8GHz, and 6GHz and the simulated gains are 3.72dBi, 7.67dBi, and 8.02dBi respectively. The VSWR at the resonance frequencies 3.2GHz, 5.7GHz, 5.8GHz, 5.94GHz, 6GHz, and 6.14GHz are 1.50, 1.38, 1.42, 1.60, 1.34 and 1.61. The VSWR is less than 2 at all resonance frequencies which shows good impedance matching. The return loss at the resonance frequencies of 5.8GHz and 3.24GHz is -19.04dB and -16.38dB respectively. The antenna has a bandwidth of 0.68GHz ranging from 5.52GHz to 6.18GHz. The proposed antenna is suitable for many wireless applications such as WLAN (5.15 – 5.35 & 5.75 – 5.8) GHz, Wi-Fi (5.15 – 5.82) GHz, and RFID (5.725 – 5.875) GHz
Enhancing Face Mask Detection in Public Places with Improved Yolov4 Model for Covid-19 Transmission Reduction
Get PDFOver the past decade, computer vision has emerged as a pivotal field, focusing on automating systems through the interpretation of images and video frames. In response to the global impact of the COVID-19 pandemic, there has been a notable shift towards utilizing computer vision for face mask detection. Face masks, endorsed by international health authorities, play a crucial role in preventing viral transmission, prompting the development of automated monitoring systems in various public settings. However, existing artificial intelligence (AI) technologies\u27 effectiveness diminishes in congested environments. To address this challenge, the study employs a meticulously fine-tuned YOLOv4 model for identifying instances of mask non-compliance in accordance with COVID-19 Standard Operating Procedures (SOPs). A distinctive feature of the training dataset is its inclusion of images featuring Muslim women with both half and full-face veils, considered compliant with face mask guidelines. The dataset, comprising 5800 images, including veil images from various sources, facilitated the training process, achieving a comparatively good 97.07% validation accuracy using transfer learning. The adaptations, coupled with a custom dataset featuring crowded images and advanced pre-processing techniques, enhance the model\u27s generalization across diverse scenarios. This research significantly contributes to advancing computer vision applications, particularly in enforcing COVID-19 safety measures within public spaces. The tailored approach, involving model adjustments, underscores the adaptability of computer vision in addressing specific challenges, highlighting its potential for broader societal applications beyond the current global health crisis
Enhancing Security in Mobile Cloud Computing: An Analysis of Authentication Protocols and Innovation
Get PDFIntroduction/Importance of Study: Cloud computing is a model facilitating ubiquitous, convenient, and on-demand network access to a shared pool of computing resources, offering flexibility, reliability, and scalability .
Objective: This study investigates authentication mechanisms in Mobile Cloud Computing (MCC) to enhance security and address emerging challenges.
Novelty statement: Our research contributes novel insights into authentication protocols in MCC, offering solutions to security issues not previously addressed.
Material and Method: The study analyzed various authentication mechanisms in MCC using NIST evaluation criteria, considering their alignment with security needs and resource constraints.
Result and Discussion: Our findings underscore the importance of selecting authentication mechanisms that balance security and performance in MCC environments, highlighting the need for ongoing innovation in security measures.
Concluding Remarks: The study emphasises the significance of robust authentication protocols tailored to MCC\u27s unique security requirements for ensuring data integrity and privacy