International Journal of Innovations in Science & Technology
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Forecasting the Impacts of Climate Variability on Cotton Production in South Punjab, Pakistan
Get PDFCotton, an important crop in Pakistan, contributes 0.8% of GDP and 4.1% of total agricultural value added. However, it faces challenges such as a reduction in area under cultivation, unfavorable weather conditions, whitefly infestation, crop stunting, bollworms, and other insect pests. The objective of this study is to identify the factors contributing to the decline in cotton production in selected districts of southern Punjab in Pakistan during the period 2004-2020. The second objective of this study was an over-time analysis of secondary data to calculate compound growth and forecast area production and average yield in Punjab, Pakistan. Data was collected from secondary sources and analyzed using SPSS and Microsoft Excel. The results showed that Rahim Yar Khan and Khanewal had no impact on production, while Lodhran had a significant impact due to the minimum temperature. A decrease in minimum temperature by one unit led to a decrease in cotton production by 21947. Pearson\u27s correlation showed a weak relationship between humidity and cotton yield in the study area. The time series analysis revealed that cotton production in Khanewal and Multan districts will increase in the future, while in Jhang, Sahiwal, Pakpattan, Vehari, and Rahim Yar Khan districts will experience a declining trend. Previous studies suggest that Pakistan’s crop production could be significantly affected by a reduction of rainfall, a 0.5-degree rise in temperature over the last three decades, and changes in the frequency of droughts and floods. This study aims to develop a policy framework involving suitable cotton varieties to improve cotton production and raise the country\u27s GDP
Development of XAI-Driven Churn Prediction Framework for Proactive Retention in Telecom
Get PDFIn the telecommunications industry, customer churn is a major problem that has a big influence on profitability and competitiveness. Current systems mostly rely on reactive strategies that are unable to prevent churn proactively. We present an intelligent, explainable AI-driven system, TeleChurnAI, which predicts churn and pinpoints its root causes. The novelty of this research lies in its integration of explainable AI and customer segmentation, providing useful information for retention strategies. To develop TeleChurnAI, a machine learning model, CatBoost, is employed to accurately predict churn, and SHAP (Shapley Additive exPlanations) is utilized to interpret model results as well as to support predictions. The prediction accuracy and interpretability of the model are assessed after it is trained on the historical telecom customer dataset, “Telco Customer Churn”. We found that TeleChurnAI offers transparency through visual explanations of churn risk factors and significantly increases the accuracy of churn predictions. Through an interactive dashboard made for CRM professionals with different levels of technical expertise, using the Shiny framework in Python, the system also divides up the customer base according to demographic and behavioral trends, allowing for targeted retention actions. In addition to helping with early intervention, this dual capability lowers marketing expenses and boosts customer loyalty. In conclusion, TeleChurnAI provides a thorough and user-centered method for telecom churn management. In the future, we aim to integrate sentiment analysis and real-time prediction in our proposed system
Impacts of Climate Variability and Urban Expansion on Groundwater Systems in Mansehra
Get PDFGroundwater serves as the principal source of domestic water supply in Mansehra District, Khyber Pakhtunkhwa, Pakistan. However, rapid urbanization and climate change have increasingly impacted groundwater quality and recharge dynamics in the region. This study investigates groundwater quality and identifies potential recharge sources under shifting environmental conditions. A total of eleven water samples were collected from wells, rivers, and surface channels across five distance-based zones (0–200 m, 201–400 m, 401–600 m, 601–800 m, and 801–1000 m) relative to the nearest river. The samples were taken in sterile, dry containers and were transported within 24 hours to the Pakistan Council of Research in Water Resources (PCRWR), Islamabad, for detailed physicochemical analysis. Parameters measured included pH, electrical conductivity (EC), total hardness, alkalinity, turbidity, calcium (Ca), and potassium (K). The findings show extensive groundwater quality deterioration in areas subject to high urban activity and climatic fluctuation. More specifically, samples from Baffa showed higher hardness, Ca, and K levels above WHO allowable limits, indicating contamination potential. On the other hand, samples from Nokot and Ichria mostly met WHO standards, with turbidity being the only parameter of concern. Comparison of analyses of well, river, and pond samples revealed rivers and surface water bodies as the preeminent sources of groundwater recharge. The findings highlight the imperative need for sustainable groundwater management practices to mitigate the adverse impacts of anthropogenic stresses and climate change in the Mansehra Basin
A Lightweight Blockchain-Enabled Trust Management Model for Secure Vehicular Communication
Get PDFVehicular Ad Hoc Networks (VANETs) are emerging as a pivotal component in intelligent transportation systems, offering safety-critical and comfort-related information to drivers and passengers. The effectiveness of VANETs relies on the timely exchange of messages between vehicles and roadside units (RSUs), where trustworthiness of shared data is paramount. Traditional centralized trust models, though efficient in information validation, suffer from single points of failure, limited scalability, and vulnerability to insider threats. This has driven a paradigm shift toward decentralized architectures, with blockchain technology standing out due to its immutable, transparent, and distributed nature. This study presents a comprehensive review of existing centralized and decentralized trust management models in VANETs, analyzing their methodologies, strengths, and limitations. By examining trust metrics, validation schemes, and message verification strategies across the literature, it identifies critical gaps in scalability, response time, and resistance to malicious behavior. Addressing these limitations, we propose a novel blockchain-based trust model named CB-RTM (Consortium Blockchain for RSU-Assisted Trust Management), an intelligent framework designed to ensure secure, verifiable, and real-time dissemination of safety messages in VANETs. The CB-RTM model integrates consortium blockchain with RSU-based validation and a Proof-of-Authority (PoA) consensus mechanism to filter and authenticate event messages using location certificates and trust scores. Unlike existing approaches, the model localizes trust updates and block propagation to geographically bounded regions, enhancing scalability and latency performance. Experimental evaluation demonstrates that the proposed CB-RTM outperforms state-of-the-art models across key metrics. The model achieves a trust accuracy of 96.2%, latency of 0.42 seconds, and throughput of 245 messages per second, while maintaining a manageable communication overhead of 11.2%. These results confirm that CB-RTM is a robust, scalable, and efficient solution for trust management in real-time VANET environment
Smog, Stress, and Society
Get PDFSmog, an escalating byproduct of climate change and rapid urbanization, poses a significant risk to public health and resilience, especially in megacities like Lahore. This study examines the psychological, biological, and social impacts of smog on young adults, situating these findings within the broader discourse on climate-induced hazards and disaster risk reduction (DRR). Employing a mixed-method approach, the research involved focus group discussions, scale development with expert validation, and large-scale public perception analysis using digital text analytics (Voyant Tools) on responses from 800 participants. Results revealed eleven major themes ranging from anxiety, mood disturbances, and impaired concentration to respiratory ailments, skin conditions, and reduced social interactions, capturing the multidimensional burden of smog exposure. Traffic emissions, loss of green spaces, and industrial pollution emerged as the most cited contributors, reflecting gaps in urban environmental management. The study’s Bio-Psychosocial (BPS) Scale (CVI = 0.93) offers a validated tool for assessing smog’s multifaceted impacts, enabling targeted policy and anticipatory interventions. These insights highlight the need to integrate air quality monitoring, green infrastructure, and public awareness into climate change adaptation and DRR strategies to safeguard both health and societal cohesion in vulnerable urban populations
Synergistic Effect of Pyrolyzed Bagasse and Trichoderma Viride for Sustainable Mitigation of Chili Southern Blight
Get PDFSoil-borne diseases like southern blight severely limit chili (Capsicum annuum L.) production, demanding sustainable and eco-friendly management approaches. This study introduces the integration of sugarcane bagasse-derived biochar with Trichoderma viride as a novel strategy for enhancing chili resistance against Sclerotium rolfsii. Biochar was produced through pyrolysis at 450°C and characterized using SEM, EDX, and XRD, revealing porous honeycomb-like structures, high carbon content, and mineral phases such as SiO₂ and CaO. Glasshouse experiments were conducted on the chili cultivar ‘Desi’ using biochar at 3% & 6% (v/v) concentrations. Biochar was either applied alone or in combination with T. viride as well as with S. rolfsii. Results demonstrated that biochar treatments significantly enhanced shoot and root growth, biomass accumulation, and physiological performance under pathogen stress. Disease severity, incidence, and mortality were notably reduced, with the greatest suppression (20%) noted in chili plants treated with 6% biochar plus T. viride. Furthermore, higher biochar doses substantially elevated levels of defense-related compounds, including phenolics, catalase, and flavonoids, indicating induction of systemic resistance. Similarly, the combined effect of biochar and T. viride was also visible under in vitro assays. Overall, the integration of biochar and beneficial fungi not only improved soil health but also strengthened host defense, offering a sustainable approach to managing southern blight. These findings highlight biochar-induced resistance as a promising component of integrated disease management in chili cultivation.
Artificial Intelligence-Augmented Intrusion Detection Systems for Advanced Threat Taxonomy in Cloud Computing Environments
Get PDFOver the past few decades, cyber-attacks have emerged as a grave form of criminal activity and a subject of intense scholarly and policy debate. The rapid proliferation of cloud computing services— particularly Software as a Service (SaaS)—has further motivated research to classify security threats and their corresponding countermeasures. Scholars have increasingly focused on the risks, vulnerabilities, and malicious intrusions inherent in such environments, with particular emphasis on MITM (MITM) attacks and their mitigation and detection mechanisms. Host-based virtual software has demonstrated considerable efficacy in detecting malware within localized environments. Building on this foundation, the present study classifies Man-in-the-Middle (MITM) attacks in SaaS platforms through the deployment of Cloud-based Intrusion Detection Systems (CIDS). Our investigation concentrates specifically on attacks that target cloud hosts deployed within SaaS infrastructures. The proposed methodology incorporates the roles of the source cloud, destination cloud, and directional flow of the attack vector. In this context, the cloud ecosystem is understood as a dynamic environment where any participating entity, equipped with sufficient technical expertise, may both launch and be subjected to sophisticated intrusions. Accordingly, adaptive CIDS monitoring architectures are essential to safeguard communication between cloud actors. Moreover, CIDS frameworks furnish modular components capable of aggregating alerts, conducting analysis, and notifying administrators of potential breaches. To further illustrate the threat landscape, we present a statistical analysis of vulnerabilities most frequently exploited in MITM scenarios. This classification not only highlights the evolving tactics of adversaries but also equips readers with a structured understanding of MITM attacks, thereby fostering greater familiarity with contemporary cloud security challenges
Interactive Impacts of Heavy Metals and Soil Amendments on Enzymatic Activities and Microbial Biomass
Get PDFBoth organic and inorganic soil additives are frequently used to increase the bioavailability of lead (Pb) and cadmium (Cd) in polluted soils, but these amendments may also affect microbial activity in soils by modifying heavy metal solubility. This research assessed the influence of different soil additives on enzymatic activity and the solubility of Pb and Cd in spiked soils. Soils were spiked with Pb (0, 1000, 1500 mg kg⁻¹) and Cd (0, 100, 150 mg kg⁻¹) artificially. Incubation experiments were carried out with various amendments, such as citric acid (CA; 0, 10 mmol kg⁻¹), ammonium nitrate (AN; 0, 10 mmol kg⁻¹), EDTA (0, 5 mmol kg⁻¹), compost (CO; 0, 10%), and titanium dioxide nanoparticles (TNPs; 0, 100 mg kg⁻¹). The microbial biomass carbon (Cmic) and dehydrogenase activity (DHA) declined by 66% and 47% in Pb₁₅₀₀, and by 54% and 35% in Cd₁₅₀ treatments, respectively. In control soil, compost addition gave the highest value of Cmic and DHA, followed by TNPs, CA, AN, and EDTA. But the mixed application of Pb, Cd, and soil additives caused an overall reduction in microbial activity. Among all the treatments, EDTA alone and in combination with Pb and Cd showed maximum toxicity to soil microorganisms
Effect of Different Drying Methods on Quality Characteristics of Wheat Grains
Get PDFWheat is one of the main staple cereals used worldwide, providing 50% of food energy and protein consumed globally. Drying, the oldest preservation technique used for agricultural products, is the removal of moisture to its certain desired requirement. The drying process aims to reduce the grain\u27s moisture content for safe storage. The present study was carried out at the Department of Farm Structures and Postharvest Engineering, Sindh Agriculture University, Tandojam, Pakistan. Freshly harvested wheat grain varieties TD-1, Imdaad-2005, and Sindhu were collected from the Latif Experimental Farm, Sindh Agriculture University, Tandojam. The wheat grain samples were subjected to three different drying methods i.e. sun drying, convective drying, and intermittent drying. Data in terms of quality for wheat grain was observed initially, and then after being subjected to different drying methods. The results revealed that moisture content, length, width, thickness, thousand kernel weight, weight loss, bulk density, germination, and drying rate for all wheat varieties decreased with respect to time in all drying methods. The results in terms of quality after drying were observed to be better for intermittent drying, followed by convective and sun drying. The farmers and grain processing industries are suggested to adopt the intermittent drying method, which enhances grain quality and reduces energy consumption
Clear Tic-AI: Detection of Dysarthria and its Severity Analysis
Get PDFDysarthria and other motor speech disorders result from abnormalities in the neural or muscular processes that actually control speech production; conversely, these disorders affect the strength, coordination, and tone of the vocal muscles that ultimately produce less intelligible speech. Because dysarthria can range from moderate distortion of articulation to severe impairment of speech, early and accurate assessment is critical. The paper proposes Clearitic AI, an automated speech analysis platform that leverages artificial intelligence to diagnose vocal disorders. It fuses Wav2Vec2 with traditional acoustic features such as Mel-Frequency Cepstral Coefficients (MFCCs), pitch estimation, and spectral descriptors. Abnormal voice classification and its severity on a framework with a sequential neural network architecture are proposed. Extensive testing of the system is performed using 10,000 recordings of voice samples from the TORGO dataset and the Mozilla Common Voice dataset. Experimental results demonstrate that the proposed model achieves a classification accuracy of 94.2% (±1.3), an F1-score of 0.943, and an Area Under the Curve (AUC) of 0.987 on the test set, thereby establishing the effectiveness of this framework for dysarthric speech detection applications