International Journal of Innovations in Science & Technology
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A Comparative Assessment of Orchards Distribution in Urban and Peri-Urban Agriculture Zones in Karachi Through RS/GIS Techniques: (2005-2023)
Get PDFOrchard farming plays a crucial role in the urban ecosystem, contributing to biodiversity and offering opportunities for organic waste reprocessing and natural resource conservation. It holds significant economic and cultural value, enhancing the urban environment. This research on orchard distribution in Karachi provides insights that can guide sustainable development, infrastructure planning, environmental protection, and policy-making. The study employs advanced remote sensing and GIS techniques to monitor and analyze changes in orchards over time. Satellite images from Google Earth Pro were utilized to detect temporal changes, with a comparative assessment of agricultural land use between 2005 and 2023. ArcGIS software was used for digitization, revealing that most orchards in Karachi are concentrated in the peripheral areas along the Malir River, predominantly consisting of sapodilla, papaya, and guava trees. These orchards contribute to sustenance, employment, and environmental improvement. However, digitization has uncovered significant changes, including the transformation of some orchard lands into industrial and built-up areas. The data generated from this process can inform sustainable land management decisions for urban agricultural areas in developing nations
Eco-Mobility in Lahore, Pakistan: Assessing the Role of Electric Vehicles in Air Pollution Mitigation
Get PDFIntroduction/Importance of the Study: This study investigates the potential of electric bikes to replace fossil fuel-powered motorbikes as a solution to reduce air pollution in Lahore. Globally, the transport sector relies heavily on fossil fuels, which are a major contributor to air pollution. In Pakistan, 23% of greenhouse gas (GHG) emissions come from road transport. Lahore alone has 4.2 million petrol-based motorbikes, significantly contributing to deteriorating air quality and posing serious health and environmental risks. Transitioning to electric vehicles, particularly in Punjab\u27s major cities like Lahore, offers a promising approach to reduce harmful air pollutants. However, Pakistan faces various challenges that hinder the rapid adoption of electric vehicles.
Novelty Statement: This research highlights key policies needed for infrastructural and technological advancements to accelerate the adoption of electric bikes. The study is unique in that it focuses on controlling emissions in Lahore by replacing petrol motorbikes with electric bikes, using empirical data specific to the city. The analysis includes a detailed examination of daily and annual emissions from petrol-based motorbikes, focusing on pollutants like carbon monoxide, hydrocarbons, and sulfur and nitrogen oxides.
Materials and Methods: A questionnaire-based survey was conducted to determine the average daily mileage and annual working days of motorbikes in Lahore. Emission data for petrol and electric bikes was sourced from secondary sources, allowing a comparison between vehicles powered by renewable and non-renewable energy. Additionally, emissions from coal-based power plants generating electricity for electric bikes were also analyzed using secondary data.
Results and Discussion: The results indicate that electric bikes powered by renewable energy produce negligible emissions compared to petrol and diesel vehicles. However, when electricity is generated from non-renewable sources, such as coal-fired power plants, the emissions remain high and continue to contribute to air pollution.
Concluding Remarks: The study recommends that policymakers prioritize renewable energy sources for powering electric bikes. It also stresses the need for public-private partnerships, tax exemptions, and cost reductions to promote electric bike schemes for the general public
Recurrent Neural Network and Multi-Factor Feature Filtering for Ransomware Detection in Android Apps
Get PDFThe market is flooded with Android Software (apps), and at the same time that number is growing quickly, and so are the many security exploits that take advantage of such apps. The effectiveness of traditional defensive systems is at risk due to the growing diversity of Android malware. This situation has sparked significant interest in improving malware detection accuracy and scalability for smart devices. By examining the Long Short-Term Memory (LSTM) method, we have developed an effective deep learning-based malware detection model for enhanced Android ransomware detection. For feature selection, eight different methods were applied. By comparing the outcomes of all feature selection procedures, we used a simple majority vote process to choose the 19 crucial characteristics. The Android Malware dataset (CI-CAndMal2017) and common performance metrics were used to assess the proposed technique. With a detection accuracy of 97.08%, our model surpasses existing approaches. We advocate our proposed method as effective in malware and forensic analysis based on its remarkable performance
The Agroforestry Potential and Analysis of Growth and Yield of different Vegetables Grown Under Olive Orchard to Mitigate Climate Change Effects
No full textOlive is a drought-tolerant plant, making it suitable for cultivation in various dry regions of Pakistan. By applying the principles and regulations of agroforestry, we can increase crop yields, thereby creating a self-sustained farming ecosystem. Agroforestry is a technique that integrates the production of trees, vegetation, and livestock on the same land to achieve financial, environmental, ecological, and cultural benefits. A field experiment was conducted on six winter vegetables—cabbage, Chinese cabbage, kohlrabi, leafy green lettuce, leafy red lettuce, and broccoli—grown under three olive orchards of different ages (10, 20, and 30 years) with varying shading capacities at the Horticultural Research Institute, National Agricultural Research Center, Islamabad. The study focused on intercropping vegetables within olive orchards of different ages. Critical parameters were monitored, and strict plant inspections were carried out during the experimentation period. Plant samples were tested for morphology and chemical composition. It was found that more vigorous olive trees significantly decreased the growth, leaf chlorophyll content, nutrient uptake, and yield of the intercropped vegetables. Maximum shading from the 30-year-old olive orchard severely reduced plant growth and yield. The extent to which growth is limited by intercropping or shade intensity may vary with the genetic makeup of different crops. The results showed that plants grown under optimal light conditions exhibited greater plant height, spread, and stem diameter, attributed to the stimulation of cellular expansion and cell division under adequate sunlight, which increases photosynthetic efficiency. Cabbage and kohlrabi were identified as the most viable crops under the experimental conditions
Analyzing the Impact of Smog on Human Health in District Lahore, Pakistan
Get PDFSmog is a term used to describe pollution suspended in humid air, consisting of dust particles of various sizes, non-metal oxides, organic compounds, and heavy metals. Exposure to these toxic compounds, in addition to cigarette smoking, is a significant factor in the development of respiratory diseases. Smog is a visible form of air pollution that results from excessive emissions of primary pollutants such as volatile organic compounds (VOCs), hydrocarbons, sulfur dioxide (SO₂), nitrogen oxides (NOₓ), and nitrogen dioxide (NO₂). These pollutants react in the atmosphere to form harmful and carcinogenic secondary smog components. Airborne chemicals that adversely impact public health include ozone and particulate matter (PM) of various sizes—PM2.5, PM2.5–10, and PM10—as well as nitrogen oxides. Special attention is given to lead, carbon dioxide (CO₂), sulfur dioxide (SO₂), and carbon monoxide (CO), with a focus on smaller dust particles (PM10 and PM2.5) because they can penetrate the lower respiratory tract. This page explores the effects of atmospheric pollutants on the onset and exacerbation of respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and lung cancer. It also discusses legislative measures implemented in various countries to mitigate exposure to harmful air pollution. Based on the survey responses, it appears that the individual may be experiencing symptoms related to respiratory, skin, and cardiac conditions, such as coughing, wheezing, and shortness of breath. They have been diagnosed with several health issues, including asthma, chronic bronchitis, pneumonia, and ischemic heart disease. Diagnostic tests such as chest X-rays and arterial blood gas (ABG) tests are likely to have been performed during their hospital stay. The individual has reported experiencing symptoms and health effects associated with air pollution or smog during their hospitalization
Breaking Down Monoliths: A Graph Based Approach to Microservices Migration
Get PDFIntroduction: The software industry has increasingly transitioned from Monolithic Architecture (MA) to Microservices Architecture (MSA) due to the significant advantages offered by MSA. A crucial first step in this migration process is the identification of suitable microservices.
Novelty Statement: This work aims to introduce an automated method for more effectively identifying potential microservices within monolithic applications.
Materials and Methods: Our approach leverages the source code to construct a frequency-based class dependency graph through graph analysis techniques. A clustering algorithm is then applied to this graph to identify optimal candidate microservices.
Results and Discussion: We evaluate the effectiveness of the proposed approach using several metrics, including the number of microservices, Newman-Girvan Modularity (NGM), and F1-Score. The results demonstrate that the approach accurately identifies candidate microservices, achieving an average F1 score of 0.88 and an average NGM score of 0.526.
Concluding Remarks: The proposed approach proves to be an effective tool for assisting developers in migrating from MA to MSA, facilitating a more streamlined transition process
Plant Disease Detection Using Computational Approaches: A Systematic Literature Review
Get PDFRapid improvements in ML and DL techniques have made it possible to detect and recognize objects from images. Computational approaches using ML and DL have been recently applied to agriculture or farming applications and are proving successful in increasing per-yield production. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use DL and ML approaches to address various plant DD concerns was undertaken in the current publication. In this work, we have reviewed 35 of the most recent DL and ML-based articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the ML and DL used in plant leaf DD. Moreover, DCNN trained on image data was the most effective method for detecting early DD. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant DD
Signal Processing Approach to Detect Non-Metallic Targets for Through Wall Imaging Using UWB Antenna
Get PDFThis paper presents a signal-processing approach aimed at detecting non-metallic targets through walls using an Ultra-Wideband (UWB) antenna configuration. Through-wall imaging holds significant importance in various fields including security, surveillance,and search and rescue operations. The proposed methodology involves the utilization of the UWB Antipodal Vivaldi antenna, known for its wide bandwidth and high-resolution imaging capabilities. The study focuses on the detection of non-metallic targets using signal processing techniques. A through-wall simulation model utilizing a UWB Antipodal Vivaldi Antenna has been developed in CST Microwave Studio. This model generates received signals in the time domain for through-wall target detection, which are subsequently processed using signal processing techniques to produce 2D images
Assessment of ML Classifiers in Complex Human Activity Recognition Using Wearable Sensors Data
Get PDFHuman Activity Recognition (HAR) is essential for understanding daily behavior patterns, and wearable sensor data serves as a reliable source for monitoring complex activities. This study uniquely evaluates the performance of nine machine learning classifiers in the context of complex human activity recognition, relying solely on wearable sensors. It offers valuable insights into classifier effectiveness for real-world applications. Data from the PAMAP2 dataset, which was collected using three wearable IMUs placed on the hand, chest, and ankle, along with a heart rate sensor, was utilized to identify six daily complex activities. A 70/30 train-test split methodology was implemented to assess classifier performance. The Random Forest (RF) classifier achieved the highest performance, boasting 93% accuracy, precision, recall, and F1-score, followed closely by the K-Nearest Neighbors (KNN) classifier, which recorded 91% across all metrics. In contrast, the Logistic Regression (LR) classifier underperformed, achieving only 55% accuracy, likely due to its limitations in handling non-linear data. These results demonstrate that RF and KNN classifiers are effective for complex human activity recognition, while linear classifiers like LR are less suitable for such tasks. Overall, the Random Forest and KNN classifiers provide reliable performance for complex human activity recognition using wearable sensors, making them excellent choices for practical applications
AI-Powered Detection: Implementing Deep Learning for Breast Cancer Prediction
Get PDFBeast cancer remains a critical global health issue, affecting millions of women worldwide. According to the World Health Organization (WHO), there were 2.3 million new cases and 685,000 deaths from breast cancer in 2020 alone. This makes breast cancer the most prevalent cancer globally, with 7.8 million cases diagnosed over the past five years. As the prevalence of breast cancer continues to rise, the need for accurate and efficient diagnostic tools becomes increasingly urgent. Artificial Intelligence (AI) has shown considerable promise in enhancing breast cancer detection and diagnosis. Over the past two decades, AI tools have increasingly aided physicians in interpreting mammograms, offering the potential for automated, precise, and early cancer detection. However, significant challenges remain, particularly concerning data imbalance in datasets—where cancerous images are often underrepresented—and the issue of low pixel resolution, which can obscure crucial details in medical images. This work utilizes a subset of the data called Mini-DDSM, a lightweight version of the Digital Database for Screening Mammography. To address these challenges, our research employed the Neighborhood Cleaning Rule (NCR) algorithm from the imbalance library, designed to mitigate data imbalance by refining the dataset through the selective removal of noisy and borderline examples. This method enhances the quality of training data, enabling AI models to learn more effectively. We developed a deep learning model that incorporates a transfer learning layer (DenseNet121), dense layers, a global pooling layer, and a dropout layer to optimize performance. This model demonstrated promising results, effectively addressing the challenges of data imbalance and low image resolution. Our approach underscores the potential of AI to significantly improve breast cancer detection and diagnosis, ultimately leading to better patient outcomes. Continued research and refinement of AI techniques will be crucial in overcoming remaining challenges and fully realizing the potential of these technologies in healthcare