International Journal of Innovations in Science & Technology
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Dynamic of Land Use Land Cover and its Impact on Land Surface Temperature (LST) Using GIS: A Study of District Mardan, Pakistan
Get PDFRapid population growth is a global issue that alters landscapes and affects environmental conditions. This study aims to assess the effects of urbanization on Land Use Land Cover (LULC) changes and their impact on Land Surface Temperature (LST) in District Mardan from 2002 to 2022. By combining remote sensing data on LULC changes with LST measurements, researchers can analyze the relationship between these variables. This analysis sheds light on how LULC changes affect local climate patterns, urban heat island effects, and the overall thermal environment. Data preprocessing was done using ArcGIS 10.8 software. After preprocessing, a supervised classification scheme was applied for 2002 and 2022, using the maximum likelihood algorithm to identify LULC changes. In 2002, the built-up area covered 165.47 km², which increased to 266.70 km² by 2022. Vegetation covers gradually declined over the same period, with a notable shift from vegetation to built-up areas. The study revealed significant changes in District Mardan, including population growth, urban expansion, and infrastructure development. These changes were influenced by various factors, including land cover types. The results of this study can be useful for regional and urban planning, as well as for managing agricultural practices in the future
Elevating Group Recommendations and Collective Decisions Through Prioritized User Activities in Groups
Get PDFGroup modeling encompasses various areas of interest, including recommendations, movie watching, exercise performance, and the formation of social media groups with similar interests. Similarly, the GRS has numerous practical applications, such as books, movies, and television program recommendations. Various collaborative techniques, such as Least Misery, Average Voting, and Most Pleasure, to name a few, have been employed to enhance group recommendations. However, these methods are not without limitations, often introducing biases and yielding irrelevant suggestions. For example, group of people watching television, the active user having a remote control is paramount. Active user(s), who engage in activities like channel switching, rating, expressing preferences, and commenting, should hold significant influence. This study proposed and integrates active user engagement and feedback into the recommendation process, by considering user activities as feedback. The proposed system employs a filtering mechanism that emphasizes the user’s activities, facilitating the prediction of relevant suggestions to group users. The experiments utilized the well-established benchmark dataset Movie Lens. The effectiveness of the proposed approach is evaluated using standard metrics such as precision, recall, and F-score. The results show that recommending active items to actively engaged user(s) significantly benefits most of the group users, yielding an improved suggestion. This study may help practitioners to build more robust recommender systems for groups
Monitoring Snow-Covered Dynamics and Impact on Climatic Change
Get PDFGlaciated areas play a crucial role in cooling the planet; however, their accelerated melting initiates a feedback loop that decreases Earth\u27s albedo, leading to further warming and increased melting. This phenomenon poses significant risks to Pakistan\u27s agricultural productivity and energy supply, particularly in the Himalayan, Karakoram, and Hindukush (HKH) mountain ranges. These regions are undergoing substantial changes due to global warming and regional climate variability. Glaciers and snow packs in these areas function as natural reservoirs, releasing vital meltwater during the summer to sustain river flows, especially the Indus River, which is essential for Pakistan\u27s agriculture, drinking water, and hydropower. This study aims to monitor the extent, mass, and distribution of snow cover in the HKH ranges to assess local vulnerability and provide a comprehensive evaluation of ongoing climate change impacts. By analyzing Landsat 5, 7, and 8’s Tier 1 Top of Atmosphere (TOA) reflectance products, the annual median snow cover from 1991 to 2020 was calculated to visualize and quantify snow cover dynamics in Hunza Nagar, Gilgit-Baltistan, Pakistan. The results revealed no significant trends in total snow cover, with only minor fluctuations and variations from the mean value, and notable reductions during strong El Niño years. These findings underscore the critical importance of glaciated areas and the threats posed by their melting. Ongoing monitoring and comprehensive regional assessments are vital to understanding the impacts of climate change on snow cover dynamics. Such efforts are essential for developing adaptive strategies to mitigate adverse effects on Pakistan\u27s water resources, agriculture, and energy systems
Comparative Assessment of Classification Algorithms for Land Cover Mapping Using Multispectral and PCA Images of Landsat
Get PDFThe advancement of remote sensing technologies and the availability of free satellite data have significantly enhanced the precision of land use and land cover (LULC) mapping, facilitating the analysis of landscape transformations and ecosystem changes. However, selecting the most suitable classifier for LULC mapping remains a complex challenge. Therefore, it is essential to evaluate the accuracy of various LULC modeling algorithms to determine their effectiveness in different applications. This study conducted a comprehensive evaluation of both supervised machine learning algorithms and traditional classification methods applied to Landsat 8 imagery with a 30-meter spatial resolution, covering the Shangla and Battagram districts in Khyber Pakhtunkhwa (KPK), Pakistan. The study focused on three classification algorithms: Maximum Likelihood Classification (MLC), Support Vector Machines (SVM), and Random Forest (RF). The performance of these algorithms was assessed on both multispectral images and composite images derived from Principal Component Analysis (PCA) and Band Ratioing and/or Normalized Indices. Additionally, the accuracy of these algorithms, when applied to different datasets, was compared with the recently released World Cover LULC product by the European Space Agency (ESA). The results indicated that the SVM algorithm outperformed the others, achieving an overall accuracy of 90.43% and a kappa coefficient of 0.8792. The MLC and RF algorithms also produced promising results, with overall accuracies of 85.58% and 88.46%, respectively. Furthermore, the study found that the overall accuracy of ESA’s World Cover LULC product was 70.67% in the study area, based on similar validation samples. These findings underscore the strengths and limitations of each algorithm, providing valuable insights into their suitability for LULC classification and the applicability of existing global LULC maps
Securing Pakistan\u27s Cyberspace Cyber Counter Intelligence Strengths, Weaknesses and Strategies
Get PDFCyberspace is fundamental in the contemporary world for economies, societies and politics. It has many advantages with plenty of disadvantages. The evolution of digital technology in Pakistan has given advancement and improved investment in information technology but it has also instigated numerous cyber threats to national security, economic grounds and infrastructure. These threats are not straightforward and as a result, a strong and more importantly integrated strategy for Critical Cyber Infrastructure (CCI) is necessary. Before embarking on the recommendations, this research aims to describe the current state of CCI in Pakistan and the key involved. They take into consideration weak points in essential infrastructures, the problems of data security and other matters of concern in the growing threat domain. One of the key findings of the study relates to the need to integrate other governments, companies and intelligence organizations to deal with these cyber threats. CCI has been developed in Pakistan to some extent; however, there are significantly vulnerable areas. Terminated businesses like electricity, finance and telecom face this problem because their technology is old and security is inadequate. While Pakistan has recently adopted legislation on the protection of personal data, the country is not very efficient when it comes to implementing such legislation. Therefore, eradicating these problems from the roots of Pakistan requires a comprehensive and multiple-faceted strategy that requires changes in policies, people, technology and international cooperation. The essence of the present paper is the proposition that if Pakistan has a CCI plan that is progressive synchronistic and comprehensive, it can safeguard its strategic assets and serve the safety of its economy and the nation’s security from the threats posed by the Information Age
Complex Human Activities Recognition Using Smartphone Sensors: A Deep Learning Approach
Get PDFHuman Activity Recognition (HAR) plays a critical role in understanding human behavior, with mobile phone sensors offering a promising approach for practical applications. This research uniquely addresses the challenge of Complex Human Activity Recognition (CHAR) using Long Short-Term Memory (LSTM) networks, advancing beyond basic activity recognition. LSTM was applied to three publicly available datasets—PAMAP2, Complex Human Activities, and WISDM—using accelerometer, gyroscope, and magnetometer sensor data. The research evaluated the effectiveness of both single-sensor (accelerometer) and multi-sensor combinations for recognizing complex activities. The study achieved 94-98% accuracy across datasets, showing that a single accelerometer sensor provides reasonable accuracy, while adding more sensors, like gyroscope and magnetometer, further boosts performance at a resource cost. The LSTM-based approach consistently outperformed traditional methods, including CNNs, in complex activity recognition, demonstrating its robustness in simplifying sensor requirements without compromising accuracy. LSTM networks offer an efficient and accurate solution for complex human activity recognition, balancing performance and resource optimization
Leveraging Cryptographic Primitives of Blockchain for Trust in Smart Systems: -
Get PDFCalculating and maintaining trust using Hyperledger Fabric in smart systems plays a vital role in mitigating various trust-related attacks. Current smart systems encounter several challenges, including dependence on centralized trust authorities, which are prone to attacks and present single points of failure, as well as the need to maintain user privacy while establishing trust. Ensuring data integrity and authenticity is equally crucial. In these systems, nodes assess the trustworthiness of other nodes based on their experiences and recommendations. However, trust calculations can be vulnerable to integrity attacks from malicious nodes, such as bad-mouthing and ballot stuffing. To address these threats, trust can be calculated and securely stored on the blockchain. We selected Hyperledger Fabric as the blockchain framework and conducted a prototype implementation of trust calculation on a reduced scale involving 10 nodes. Hyperledger Fabric, being a private, permissioned blockchain, is suitable for decentralized trust calculations and storage in smart devices. We simulated a healthcare scenario within an HLF network, demonstrating secure trust calculation among IoT devices. The results indicate that leveraging the cryptographic properties of blockchain significantly enhances the overall security and trustworthiness of smart systems
Exploring Character-Based Stylometry Features Using Machine Learning for Intrinsic Plagiarism Detection in Urdu
Get PDFPlagiarism detection in natural language processing (NLP) plays a crucial role in maintaining textual integrity across various domains, particularly for low-resource languages like Urdu. This study addresses the emerging challenge of intrinsic plagiarism detection in Urdu, an area with limited research due to the scarcity of datasets and model resources. To bridge this gap, our research investigates the use of character-based stylometric features in combination with machine learning (ML) and deep learning (DL) models specifically designed for Urdu text analysis. We conducted a series of experiments to evaluate the performance of several classifiers, including Random Forest, AdaBoost, K-Nearest Neighbor (KNN), Decision Tree, Gaussian Naive Bayes, and Long Short-Term Memory (LSTM) networks. Our results show that KNN and LSTM achieved the highest accuracy at 74%, with KNN outperforming the others in terms of F1-score (64.3%), highlighting its balanced performance across accuracy, precision, and recall. AdaBoost followed closely with an accuracy of 73% and a precision of 77.5%, although its F1-score was slightly lower at 63.6%. These findings emphasize the need for specialized approaches in NLP for Urdu, demonstrating that tailored ML and DL techniques can significantly improve intrinsic plagiarism detection in low-resource languages
Deep Learning-Based Image Captioning for Visual Impairment Using a VGG16 and LSTM Approach
Get PDFVisually impaired persons frequently have trouble understanding their environment. which affects regular tasks like reading signs, navigating their surroundings and recognizing things. Providing precise and timely image descriptions is crucial to improving their comprehension of their surroundings. Even if they work, traditional image captioning techniques frequently fail to provide clear, understandable explanations. Recent developments in deep learning present fresh chance to enhances to enhance picture captioning. In this sector, long short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) have become indispensable instruments. This research focuses on applications like VGG16 and Resnet models, data augmentation and transfer learning with a custom dataset to create such kind of captioning system providing original setup for precise context retrieval. Additionally, the system utilizes a text-to-speech functionality so users can listen to their responses if they are visually impaired. The highest accuracy obtained by the model is 0.9106 and validation loss was 0.1766. On randomly chosen set data experiments are conducted, there were significant differences between the BLEU scores we observed, ranging from 0.7788, to a perfect score of 0.1, indicating a diverse range of captions accuracy. This research shows how the adopted more sophisticated CNN models along with text-to-speech can improve image captioning systems by offering visually impaired detailed and meaningful descriptions
Empowering Growth: Implementation of Sustainable Software Requirement Engineering Practices in Pakistan
Get PDFIntroduction/Importance of Study: Sustainability must be integrated into Software Requirements Engineering due to the environmental implications of software systems.Novelty Statement: This research addresses the current gap in sustainable Software Requirements Engineering (SRE) by providing guidelines for integrating sustainable practices into software development.Material and Method: An online survey was conducted using self-developed questionnaires designed to gather information on current sustainability practices in Software Requirements Engineering (SRE) among software professionals. The questionnaires, distributed via Google Forms, aimed to capture respondents\u27 perspectives on the relevance of sustainable practices in the field.Result and Discussion: The findings indicate that active stakeholder engagement, the use of energy-efficient algorithms, and the establishment of continuous improvement procedures are crucial for sustainable Software Requirements Engineering (SRE). Additionally, financial incentives and well-defined criteria for evaluating environmental impact emerged as significant factors. Among the successful practices recommended for integration into software development are audits, training programs, and the adoption of renewable energy practices.Concluding Remarks: Incorporating sustainability into Software Requirements Engineering (SRE) enhances environmental sustainability and supports organizations\u27 Corporate Social Responsibility (CSR) objectives, positioning them as key contributors to sustainable software engineering