Python-Based Land Suitability Analysis for Wheat Cultivation Using MCE and Google Earth Engine in Punjab-Pakistan

The present study aims to examine the suitability of wheat crops in the four districts of Sheikhupura, Gujranwala, Hafizabad, and Nankana Sahib by conducting a thorough examination of various environmental parameters. The study utilizes the Google Earth Engine and advanced mapping techniques to employ a comprehensive Land Use and Land Cover (LULC) categorization, effectively capturing the prevailing terrain characteristics. The integration of temperature-based and soil-based suitability maps provides a comprehensive understanding of the intricate geographical patterns governing the growth circumstances of wheat. The study highlights a significant finding regarding the identification of very appropriate zones, which encompass around 28% of the total land area (4243 square kilometers) out of complete study site. These zones are particularly noteworthy as they emphasize places that are best for the growing of wheat. Approximately 45% (6819 square kilometers) of the overall land area is classified as moderately suitable, while 15% (2273 square kilometers) of the land area is categorized as less suitable. Furthermore, 16% of the total land area, encompassing 2444 square kilometers, is deemed unsuitable. The rigorous examination of soil parameters, such as pH, drainage, electrical conductivity, and soil type, contributes to a comprehensive comprehension of the soil-related elements that influence the adaptability of wheat crops. The study utilizes a Classification and Regression Tree (CART) methodology to classify crops, resulting in accurate outcomes with a ground truthing accuracy rate of 82%. This study employs a comprehensive approach by integrating temperature and soil-based data to provide a suitability map that enhances the identification of places suitable for wheat growing. Notwithstanding the accuracy of the findings, the research acknowledges certain constraints, including the necessity for heightened farmer consciousness and the incorporation of climate change ramifications. This study offers a comprehensive framework for sustainable agricultural planning, focusing on identifying certain regions that are most suitable for wheat growth. The findings of this research will serve as a valuable resource for guiding future initiatives and decision-making processes related to agricultural development in the studied area

Synergizing Digital Twin Technology for Advanced Depression Categorization in Social-Media through Data Mining Analysis

The progression from negative emotions to depression is a significant concern, marked by persistent sadness and an inability to cope with challenging circumstances. Regrettably, it can lead to the extreme step of suicide. According to the World Health Organization (WHO), 4.4% of the global population currently grapples with depression. Shockingly, 700,000 individuals worldwide took their own lives in 2023, and this tragic number continues to escalate. Our objective is to detect signs of depression in individuals through their social media posts, SMS, or comments. We collected nearly 10,000 pieces of information from Twitter comments, Facebook posts, and remarks. Employing data mining and machine learning algorithms has proven instrumental in swiftly discerning individuals' emotional states. To predict depression versus non-depression, we employed six classifiers, with support vector machines (SVMs) demonstrating the highest accuracy. A comparison between SVM and Naïve Bayes revealed that Naïve Bayes yielded superior results in our study

Enhancing Mobile Efficiency: A Cloud-Powered Paradigm for Extended Battery Life and Enhanced Processing Capabilities

In an interconnected world where mobile phones are essential to everyday operations, the constraints of these devices in terms of processing power, memory, storage, and energy efficiency are becoming increasingly apparent. This research introduces an innovative solution by integrating Mobile Cloud Computing (MCC) to address these challenges. The research focuses on the creation of an Android application called "ServiVerse" that efficiently drains the phone's battery to imitate real-world conditions. The software is accompanied by a Firebase-connected battery optimizer, which provides users with complete insights into battery state, cleaning history, and graphical representations of performance. The system's distinguishing feature is outsourcing power-intensive operations to a cloud server, resulting in increased energy efficiency and battery life. The study demonstrated successful battery optimization tactics adapted to individual users, such as the amount of cache and RAM deleted and storage space freed up on the mobile devices. This strategy has proven to be vital in addressing a key concern about background processing and the loss of power generation on mobiles, which is providing users with more efficient and longer-lasting battery life

Ransomware Resilience: A Real-time Detection Framework using Kafka and Machine Learning

Ransomware has emerged as a prominent cyber threat in recent years, targeting numerous businesses. In response to the escalating frequency of attacks, organizations are increasingly seeking effective tools and strategies to mitigate the impact of ransomware incidents. This research addresses the pressing need for real-time detection of ransomware, offering a solution that leverages cutting-edge technologies. The surge in ransomware attacks poses a significant challenge to the cybersecurity landscape, compelling organizations to adopt proactive measures. Recognizing the urgency of the situation, this study motivates the exploration of an innovative approach to ransomware detection. By utilizing advanced tools such as Apache Kafka and Spark, we aim to enhance detection capabilities and contribute to the resilience of businesses against cyber threats. Our methodology employs the Kafka tool and Spark for real-time identification of ransomware exploits. The research utilizes the CIC-MalMem-2022 dataset to develop and validate the proposed model. The integration of Apache Kafka with traditional machine learning techniques is explored to improve the accuracy of cyber threat detection, offering a comprehensive and efficient solution. The implemented model exhibits a commendable detection rate of 95.2%, demonstrating its effectiveness in identifying ransomware attacks in real-time. The combination of Apache Kafka's streaming capabilities and established machine learning methodologies proves to be a potent defense against the evolving landscape of cyber threats. In conclusion, our research provides a robust and practical approach to combating ransomware threats through real-time detection. By leveraging the synergy of Kafka and machine learning, organizations can fortify their cybersecurity defenses and respond proactively to potential ransomware exploits. This study contributes valuable insights and tools to the ongoing efforts in enhancing cyber resilience

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

Public 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's 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

Flood Inundation Modeling and Damage Assessment in Lahore Using Remote Sensing

Introduction: Ravi River has a great contribution to the glorious history of Lahore City, the second biggest city in Pakistan. But similar to all rivers of Pakistan, River Ravi occasionally experiences extreme floods. During the past 100 years, two extremely high floods created devastation in Lahore City, which caused enormous loss of properties and lives. To save the main metropolitan areas of Lahore in both these floods, the Shahdara Breaching section, on the western bank of the river, was operated. The potential of loss due to floods has increased even more owing to the rise in population, industrialization, and spring of unplanned settlements in the floodplain of the river. Importance of Study: This research provides a solution that has the potential for long-term effects in flood management, hygienic improvement of the area, planned urban development around the river, and improvement of sub-surface water quality. Novelty Statement: The present study is focused on determining the flood damage assessment using advanced geospatial techniques with HEC-RAS applications. Materials and Methods: The reach of the Ravi River is from Shahdara to Balloki. Flood frequency analysis was performed to calculate a flood return period of five and fifty years. Hydraulic modeling of the river on HEC-RAS is used to find river capacity, its Validation, calibration, assessment of hydraulic capacity, flood inundation extent, and depth analysis. Results: It is concluded that a flood of 3643.97 cumecs magnitude corresponds to 5 years return period and 7406.699 cumecs magnitude corresponds to 50 years return period. If the same phenomena occur in a repeating manner, then the built-up settlement near Ravi can meet alarming threats. According to the maximum likelihood classification, the damage assessment was mapped wherein the results show that the buildup area was 15657 acres, the water body was 7059.246 acres, the cultivated area was 38395.3 acres, and uncultivated 59464.51 acres were affected. Conclusion: The solution can also address the problems arising due to changes in river course and depletion of natural habitat. Recommendations: However, along the Lahore City, the required width is not available. In this condition, an engineering solution is mandatory to pass the flood. Channelization can be proposed to create the width of the river. The reclaimed land should be used for high-quality urban development to increase revenues. For the sake of channel stability, a detailed sediment study should be done

Global Climate Change Adaptation: Mitigating Flooding Impacts in Pakistan

Climate change is indeed a wide-reaching problem with noteworthy consequences. The climate in Pakistan has been experiencing a quick-changing pattern, accompanied by an increase in the intensity and frequency of extreme events due to global warming. The capacity of people to adapt to climate change is crucial in reducing its impacts. Over the past decade, the irregular incidents of weather events such as floods, droughts, heat waves, and cyclones have had a significant impact on the economic growth of the country. The main goal of this study is to assess how well the local community is able to adapt to the challenges posed by climate change. The study was specifically conducted in Mianwali district, Punjab province, Pakistan focusing on this specific location allows for a more in-depth analysis of the adaptive capacity of its local community to climate change. A thorough survey of the district was conducted, and the responses of the people were recorded through questionnaires and interviews. People were asked about their views on climate change and the adaptive strategies they are implementing to tackle its effects. The findings unfolded the fact that the limitations of being unaware of environmental issues are not only the lack of education but also the financial constraints are there. The study also explained that the residents of Mianwali who are aware of climate change and flood trends are more concerned about growing and using a variety of crops as a resilience tactic. Although there is a greater number of people who are not even aware of climate change and the association of floods with it, the public also claimed that the local authorities are not providing them with any information in time. So, the results suggest a clear insight for the stakeholders and policymakers to manage flooding and climate change by providing the people with crucial information beforehand and managing the situation by suggesting and implementing multiple adaptive measures

Remote Sensing-Based Prospectivity Maps Generation for Exploration of Minerals in Pakistan Using Machine Learning Techniques

The objective of this study is to generate and compare prospectivity maps that show the presence of Limestone in a specific area using remotely sensed data and machine learning techniques, in order to determine the most precise map that accurately depicts the presence of Limestone in that area. Remotely sensed data often utilize machine learning techniques to identify mineral formations and map geological features. Furthermore, machine learning techniques can also be used to generate prospectivity maps for mineral exploration. In this study, we utilized band ratios and principle component analysis (PCA) in conjunction with machine learning techniques to effectively identify Limestone formations and generate prospectivity maps for Limestone exploration using satellite imagery. Support Vector Machines (SVM) and Neural Networks (NN) were the machine learning techniques utilized on multispectral imagery from Sentinel-2 and Landsat-8. To assess the accuracy of the identification, the confusion matrix and kappa coefficient were employed. It was determined that the accuracy of the Neural Networks (NN) techniques was significantly better than the accuracy of the Support Vector Machines (SVM) techniques. The Neural Networks (NN) achieved an accuracy of 94.92% with a kappa value of 0.929, whereas the Support Vector Machine (SVM) had a maximum accuracy of 88.39% with a kappa value of 0.845. These high levels of accuracy and kappa coefficient values suggest that these machine techniques hold great potential for geological mapping and mineral exploration. The generated prospectivity maps can assist geologists and mining companies in identifying areas with a high potential for Limestone exploration, thereby reducing exploration costs and time

Applications of Artificial Intelligence in Various Traits of Life

Knowledge Administration (KA) is the method by which an organization creates, shares, applies, and manages its information and knowledge. Although conventional KA has evolved throughout the years, documentation remains its bedrock principle. The considerable shift towards remote and hybrid working, however, has shown the limitations of conventional practices. Artificial intelligence (AI) will close these knowledge gaps and alter the ways in which KA is converted and knowledge is managed. This article reviews research on artificial intelligence (AI) and Knowledge Administration (KA), focusing on how AI can help to improve their KA strategies. In light of the existing literature critical review analyses the most up-to-date methods by analyzing both theoretical and applied works. In addition, the analytical framework presented below is useful for imagining new lines of inquiry and ways to enhance the quality of existing ones

An Enhanced Authentication Scheme for Ensuring Network Devices Security and Performance Optimization

In the technology world, the wireless network is more flexible and adaptable compared to the wired network. Because it is easy to install and does not require cables. Also, there have been many recent advances in the area of WNs (Wireless Networks), which have undergone rapid development. WNs have emerged as a prevailing technology due to their wide range of applications in every field of life. The WNs are easily prone to security attacks since once deployed these networks are unattended and unprotected. In networks, authentication is a well-explored research area. Recent advancements in networks and ubiquitous devices have meant that there is a need to explore the area of authentication with a new perspective. This study explores authentication schemes and their adoption in network-connected devices. The research will study how a wide variety of devices like those in IoT, WSN, industrial IoT, and wearable healthcare devices establish authentication. The focus of the study will be on high levels of security with an algorithm that has a small footprint. The scheme will be studying the design of a lightweight and secure authentication framework for network-connected devices. The proposed scheme provides extended security features while minimizing wireless communication security challenges. The final results will validate the authenticity of this scheme