International Journal of Innovations in Science & Technology
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Artificial Intelligence-Based Approach for The Recommendations of Mango Supply Chain
Get PDFThis study utilizes a comprehensive dataset that encompasses variables reflecting temperature, humidity, precipitation, inventory levels, transportation modes, freshness scores, and ripeness scores. Compiled from various mango farms across different markets, this dataset provides a robust foundation for our analysis. To develop predictive models, we employed several machine learning algorithms, including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests (RF), and Decision Trees (DT). We divided the dataset into training and testing sets, using an 80-20 split for training and testing subsets, respectively. Model performance was evaluated using metrics such as accuracy, precision, recall, and F1 score. Our results indicate that Random Forests outperformed other models, achieving the highest accuracy, precision, recall, and F1 scores. A feature importance analysis revealed specific features that contributed significantly to the performance improvements of the model. These insights into feature importance can aid in refining the model\u27s performance, making feature importance analysis a valuable component of model evaluation
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
A Blockchain-Based Framework for Secure Public and Sealed-Bid Auctions with AES Encryption
Get PDFE-auctions are a widely adopted form of e-commerce, enabling direct bidding over the Internet. Traditionally, intermediaries play a crucial role in facilitating the auction process, leading to increased transaction costs and potential reliability issues. This paper proposes a blockchain-based solution to enhance transparency, reduce costs, and improve the security of both public and sealed-bid e-auctions. The proposed framework leverages smart contracts to automate key auction parameters such as the auctioneer’s address, start and end times, current winner’s address, and the highest bid, ensuring secure and transparent transactions. Advanced Encryption Standard (AES) is incorporated to encrypt sensitive auction data, offering robust protection against unauthorized access. The evaluation of this blockchain-based e-auction framework demonstrates significant improvements over traditional systems, including enhanced security (zero security incidents versus 15 per year in traditional systems), increased transparency (100% transaction visibility), and substantial cost reduction (70% reduction in operational costs). Additionally, the system’s scalability, efficiency, and reliability are validated, with performance improvements such as a 1400% increase in transaction throughput and a 75% reduction in auction duration. This research highlights the transformative potential of blockchain technology in modernizing e-auctions, offering a more secure, efficient, and cost-effective alternative to traditional auction platforms
AlzheimerNet-V3: Automated Deep Learning Approach for Detecting Alzheimer\u27s Disease
Get PDFIntroduction/Importance of Study:
Alzheimer’s Disease (AD) stands as the highly prevalent form of dementia, culminating in a progressive neurological brain disorder characterized by deteriorating memory function and impaired daily activities due to brain cell damage. This singular ailment is both unique and fatal, underscoring the critical importance of early detection worldwide. Timely identification holds promise in preemptively addressing the future challenges faced by numerous individuals.
Novelty statement:
By scrutinizing the disease\u27s ramifications via MRI imagery, Artificial Intelligence (AI) technology emerges as a valuable ally in categorizing AD patients, thus aiding in prognosticating the onset of this debilitating illness. In recent years, AI from Machine Learning (ML) tactics have proven instrumental in the diagnostic landscape of AD. This study employs a transfer learning methodology to accurately identify Alzheimer\u27s patients using MRI examination. Specifically, we introduce an adapted deep learning model dubbed AlzheimerNet-V3, leveraging a tailored version of the Inception v3 architecture.
Material and Method:
Our investigation encompasses comprehensive experimentation and assesses the efficiency of AlzheimerNet-V3 in collaboration with further pre-trained specimens. Notably, AlzheimerNet-V3 achieves the conclusion of accuracy, the outcome of precision, recall, and development of F1-score was computed as 94.06% for all traits. Furthermore, comparative analysis against contemporary techniques underscores the efficacy of AlzheimerNet-V3 for Alzheimer\u27s detection, highlighting its reliability for real-time implementation
Investigating Threats to ICS and SCADA Systems Via Honeypot Data Analysis and SIEM
Get PDFSupervisory Control and Data Acquisition (SCADA) and Industrial Control Systems (ICS) are crucial for managing essential infrastructure, but their exposure to the internet has made them vulnerable to cyber threats, which can lead to significant consequences. This study presents an innovative approach to investigating cyber threats to SCADA and ICS systems by combining open-source honeypot deployment, log analysis, and integration with open-source SIEM solutions to enhance threat detection capabilities and incident response. A Conpot honeypot was deployed in a containerized environment on a cloud platform and exposed to the internet to collect real-world threat data, which was then analyzed by the Wazuh SIEM solution and integrated with TheHive for security orchestration and automated response. The analysis of the honeypot logs and SIEM alerts revealed various types of attacks, including brute force login attempts, reconnaissance and vulnerability scanning, and unauthorized access attempts, originating from multiple countries and targeting different industrial protocols. The integration with TheHive enabled the creation of playbooks for automating response actions, such as blocking malicious IP addresses or isolating infected systems. The study demonstrates the effectiveness of this combined approach using open-source tools in protecting critical infrastructure and enhancing cybersecurity posture for SCADA and ICS systems
Impact of Urbanization on Land Use Land Cover and Urban Climate, using Spatio-temporal Techniques: A case study of Islamabad, Pakistan
Get PDFAn increase in urban population has been considered a major challenge over the past few years, especially in developing countries like Pakistan. It reduces vegetation area that directly affects land surface temperature (LST) and thus causes major changes in urban climate. This research mainly focuses on the surface temperature of Islamabad using LULC, LST, and Normalized Difference Vegetative Index (NDVI) as major parameters. This study spans over four years i.e., 2019, 2020, 2021, and 2022. The land use land cover maps are obtained from ESRI Sentinel 2 Land Use Land Cover Explorer. In contrast, LST maps are obtained from Level 2 Sentinel 2B Sea and Land Surface Temperature Radiometer (SLSTR) sensor LST product. NDVI is calculated using Sentinel 2B bands 4 and 8 respectively. The resulting LULC maps show that the vegetation area decreases by 7% and the built-up area increases by 8% from 2019 to 2022. Moreover, the area of dense vegetation decreased from 6.01% to 0.17% from 2019 to 2022 shown by NDVI maps. The study further reveals that the built-up areas exhibit higher LST than other classes. This is validated through Pearson’s Correlation Coefficient between LST and NDVI which shows a negative correlation of -0.41. This research concludes that the built-up area and rangeland are increasing during the studied years while the area of vegetation and bare ground is decreasing in Islamabad. This decrease directly influences LST and consequently the climate of the area. This should be mitigated by adopting such sustainable plans that involves building green alternatives into urban management
Impact of Changing Climate on Floristic Composition And Ecological Characteristics of Sheenghar Range, District Karak, Pakistan
Get PDFIntroduction: The present study assesses the floristic composition and ecological features of plant resources in the Sheenghar range hills, District Karak, Pakistan, highlighting the area\u27s biodiversity and the impacts of climate change.
Novelty Statement: This research uniquely addresses the detailed floristic inventory and ecological classification of plant species in Sheenghar, providing insights into species adaptation to climate stress.
Material and Method: Field surveys were conducted to collect and identify plant species, followed by classification into families and life-forms. The study involved analyzing plant species composition, classification based on habitat, life-form categories and leaf size spectra.
Result and Discussion: The study identified 185 plant species across 49 families, with Asteraceae being the largest family (19 species). Herbs dominated the area (65.40%), followed by shrubs (18.91%), trees (14.59%), and parasites (1.08%). The predominant life-form class was therophytes (47.45%), with microphylls being the most common leaf size (32.43%). The findings indicate significant diversity but also reveal the severe impact of climate change on the flora, necessitating further research to understand species survival under such stress.
Concluding Remarks: The Sheenghar range hills\u27 flora is diverse but vulnerable to climate change, emphasizing the need for continued ecological studies and conservation efforts
Analyzing Privacy in Frank Lloyd Wright\u27s Prairie Style Homes Through Syntactic Methods using “A Graph” and Depth Map X Softwares
Get PDFFrank Lloyd Wright\u27s Prairie Style homes, designed across the United States, showcase his unique architectural approach. This study examines how Wright\u27s designs interact with environmental conditions, focusing on privacy in eight Prairie Style homes. Detailed floor plans and architectural evaluations were analyzed using space syntax tools to assess spatial connections. The results show that Wright prioritized bedroom privacy, with lower integration values indicating seclusion. Public areas like living and dining spaces had higher integration values, promoting connectivity. The study confirms that bedrooms in Wright\u27s Prairie Style homes are intentionally designed as private spaces, with some exceptions. These findings highlight the importance of layout morphology in creating private zones within open-plan layouts. This research sheds light on Wright\u27s innovative approach to balancing privacy and openness in residential architecture
New Bovid (Artiodactyla) Fossils from the Siwaliks of Pakistan: Reviving a Lost World
Get PDFThis research investigates new fossil specimens of bovids (Artiodactyla) from the Dhok-Pathan Formation in the Siwalik region of Pakistan, a crucial site for understanding South Asia\u27s paleoecology and evolutionary history. This study provides new insights into the taxonomy and diversity of Siwalik bovids, addressing gaps in the fossil record and contributing to a more comprehensive understanding of their evolutionary relationships. In this paper, new dental elements of bovids were recovered (seven specimens were collected) comprised of both upper and lower dentitions have been recovered from the most fossiliferous sites i.e., Dhok Pathan and Hasnot villages, in Potwar foreland basin of Himalayas in Pakistan. On the basis of comparative morphology and the precise measurements of these specimens refer to the mandible of Selenoportax vexillarius, rest of all are molars and premolars of Pachyportax latidens, Pachyportax nagrii and Kobus porrecticornis. All the new dental material is documented in this research belongs to Dhok Pathan Formation of upper Siwaliks Group. The stratigraphic layers, encompassing various depositional environments such as river channels and floodplains, provide insights into the chronological and environmental contexts of the fossil assemblages. Comparative analysis with fossils from other regions ensures the accuracy and relevance of the findings, contributing to a refined understanding of the evolutionary history and biogeographic patterns of these species. The study also documents the paleoecology of the region, indicating a grassland and woodland biome that supported diverse bovid species. These findings underscore the Siwalik region\u27s significance as a key site for studying the evolutionary history of Artiodactyla and provide valuable data for future paleontological and conservation studies. The examined fauna suggests a vast and an open landscape with intermittent dry and flood seasons, creating a mosaic of ecotonal habitats with numerous niches. This research enhances our knowledge of the Siwalik region\u27s past biodiversity and environmental changes, emphasizing the need for continued exploration and analysis of its fossil record
A Wastewater Treatment Using Constructed Wetland and Sustainable Climate Change Mitigation
No full textIntroduction/Importance of Study: Addressing wastewater treatment and sanitation challenges is particularly crucial in rural areas experiencing environmental stress. As interest in recycling wastewater grows and water scarcity becomes more pressing, constructed wetlands emerge as a cost-effective solution, especially in arid regions.
Novelty Statement: This study examines a constructed wetland at Mehran UET that effectively treats wastewater while promoting sustainable water reuse. By reusing water and functioning as a carbon sink, this approach addresses water scarcity and helps mitigate climate change effects.
Material and Methods: Water samples were collected from selected locations within the constructed wetland, chosen for their effectiveness in contaminant removal. Key wastewater parameters—total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and biological oxygen demand (BOD)—were measured for these samples.
Results and Discussion: The removal of total suspended solids was observed to decrease from an average of 31 mg/l in the influent to 21 mg/l in the effluent. BOD and COD concentrations decreased from 137 mg/l to 99 mg/l and from 212 mg/l to 131 mg/l, respectively. Nitrogen concentrations in the influent were 20 mg/l, with removal to 11 mg/l in the effluent. Phosphorus removal was observed to reduce from 23 mg/l to 12 mg/l.
Concluding Remarks: Constructed wetlands enhance community resilience to climate change by offering decentralized, flexible water management solutions tailored to local conditions and climate scenarios. They diversify water sources and reduce dependence on traditional supplies