International Journal of Innovations in Science & Technology
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Prediction of Molecular and Physical Properties of Non-small Cell Lung Cancer (NSCLC) Drugs using Mathematical Modelling and M-Polynomial Indices
Get PDFThe computation of M-Polynomial indices for Erlotinib, a tyrosine kinase receptor inhibitor and most widely recognized anti-cancer drug for the treatment of patients with NSCLC and advance pancreatic cancer is the main focus of this study. In order to efficiently calculate these M-polynomial indices, we used a graph-based method which renders use of the edge partitioning technique based on adjacent matrices and vertex degrees. Using Python software, we applied numerous regression models, such as numerous Linear Regression (LR), Elastic Net Regression (ENR), Lasso Regression (LR), Ridge Regression (RR) and Support Vector Regression (SVR), to develop Quantitative Structure-Property Relationships (QSPR). Based on the M polynomial indices, these models were utilized to forecast the physical properties such as melting point, enthalpy of vaporization, molar refractivity, molar volume, and polarizability, molecular weight, molecular mass, surface area, chemical hardness of NSCLC medications. According to our research, the M-polynomial indices predict these physical attributes with remarkable accuracy, providing crucial information on structural traits that maximize anticancer effectiveness. Additionally, we suggested predictive models for every physical attribute examined, proving the value of the M-polynomial index in comprehending molecular behaviour and directing the creation of innovative therapeutic medicines. This study not only facilitates the accurate prediction of physical properties for known NSCLC drugs but also holds the potential to fasten the novel drug discovery and development, uncharacterized anti-cancer compounds, thus contributing to the advancement of cancer therapeutics
A Framework for the Prediction of Parkinson’s Disease Using Agentic Artificial Intelligence
Get PDFParkinson’s disease (PD) is a progressive neurodegenerative disorder that is difficult to diagnose, particularly in its early stages. Subtle, slowly evolving symptoms often delay confirmation, reducing opportunities for timely intervention that could improve outcomes and quality of life. Conventional diagnosis relies largely on clinical observation, which can be subjective and insufficiently sensitive for early detection. This thesis proposes an Agentic Artificial Intelligence (AAI) framework for early PD detection and severity assessment using voice-based biomarkers. Biomedical voice parameters are leveraged because vocal changes can reflect early neurological impairment. Two publicly available Kaggle datasets containing voice recordings from individuals with PD and healthy controls are used to train and evaluate the models. For detection, an XGBoost classifier achieves 94.68% accuracy with strong discriminative performance. For severity estimation, XGBoost regression models predict motor and total Unified Parkinson’s Disease Rating Scale (UPDRS) scores with high agreement to clinically reported measurements. A key contribution is an agentic decision-making layer that autonomously interprets model outputs, performs disease staging, and generates stage-dependent monitoring and treatment recommendations. Unlike conventional predictive pipelines that stop at numerical outputs, the proposed system translates predictions into actionable clinical insights to support structured decision-making. Experimental results indicate that the framework can detect PD and estimate severity effectively from non-invasive voice data, highlighting the potential of AAI for earlier diagnosis, personalized monitoring, and intelligent clinical decision support in healthcare. The multi-layer design supports modular updates to models and agent policies, enabling telehealth deployment and longitudinal tracking as additional voice samples become available over time
Fabrication and Installation of Automatic Water Level Recorder through Global System for Mobile (GSM)
Get PDFThere are so many factors that contribute to water stress, including poor management of water distribution. These fluctuations are important to be known, as the properties of lake and river shores are significantly affected by the changes in water levels. An automatic water level recorder in this condition is essential for proper distribution of water to the fields, and for researchers to get the data via mobile. A system comprising of an Arduino Nano (open source), water-level sensor (float/magnetic sensor and ultrasonic sensor), and GSM module was proposed in this study to monitor the water level of a water body. The device performed very well in both good network areas, and bad network areas with an R2 value of 1.0 for the float sensor, and 0.9996 for the ultrasonic sensor. All the sensors were reliable and accurate, whereas in case of bad network areas the SMS received was delayed at an average of 5.7 minutes. This delay can only cause some issues when the data is needed on an immediate basis. The study concluded that the device built is reliable and can be used for the real-time monitoring of water levels
Impact of Rhizospheric and Phyllospheric Mycobiota on Plant Health of Tomato
Get PDFTomato (Solanum lycopersicum) is a significant crop produced globally but suffers from numerous biotic and abiotic stresses when cultivated in fields. Among all the biological stresses, fungal diseases cause a sharp decline in yield and quality but may remain non-pathogenic and symptomless under certain fungal species throughout the plant\u27s entire life cycle. This work aimed to isolate and purify the mycobiota from various parts of the tomato plant—stem, root, fruit, leaf, and rhizospheric soil—to determine the fungal communities present. Morphological and molecular identification established the presence of various fungal species, including Aspergillus fumigatus, Acremonium spp., Pythium spp., Geotrichum candidum, Aspergillus parasiticus, Aspergillus carbonarius, Aspergillus terricola, Aspergillus flavus, Aspergillus oryzae, and Alternaria alternata. The density and distribution of these fungi varied among different plant parts and soil, with A. fumigatus showing the highest frequency (80%) among all isolates. Fungal diversity analysis revealed notable differences in species richness and evenness across plant parts. The rhizospheric soil showed the highest fungal diversity (Shannon index = 2.31), followed by roots (2.05), while the leaf and fruit tissues exhibited lower diversity indices. The Simpson\u27s index values also confirmed greater dominance and lower evenness in aboveground plant parts, indicating a more selective fungal colonization. A heat map was constructed to visually compare diversity metrics across plant parts. Moreover, the effect of microbiomes on tomato plant health, especially on chlorophyll content in the field, was also examined. The results indicate that tomato plant mycobiota play a positive role in plant health based on their interaction. Further studies need to be conducted to investigate the specific possible positive impact of individual fungal species and their interactive effect on plant health of tomato crops
Improving Cardiovascular Disease Prediction Accuracy with Three-Way Decisions
Get PDFCardiovascular Disease (CVD) is a leading cause of death worldwide, making accurate and early risk prediction crucial for better patient outcomes. Traditional CVD prediction models often rely on binary decision-making, which struggles with uncertain or borderline cases, leading to misclassification and ineffective treatment strategies. This research proposes an advanced predictive model that combines machine learning algorithms with a three-way decision approach to improve the accuracy and reliability of CVD risk assessment. The three-way decision model, based on rough set theory, divides decisions into three categories acceptance, rejection, and deferment—allowing for more detailed and informed predictions. Using the Cleveland Heart Disease dataset, this study applies machine learning techniques such as Random Forest (97.14% accuracy), Logistic Regression (91.30% accuracy), Naïve Bayes (88.24% accuracy), and Support Vector Machine (89.74% accuracy) to evaluate the model’s effectiveness. The results show that integrating three-way decisions with machine learning improves predictive performance, especially for unclear cases, enhancing clinical decision-making. However, the model’s reliance on dataset quality and threshold selection poses some limitations that need further investigation. This research introduces an intelligent and flexible approach to CVD prediction, which could reduce diagnostic errors and support early interventions for high-risk patients
A Comparative Evaluating Auditing Tools for Unverified Smart Contracts on Ethereum Blockchain
Get PDFThe Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain
Development of a Machine Learning-Based Predictive System For Classifying Psoriasis
Get PDFPsoriasis is a chronic autoimmune skin condition characterized by inflamed, flaky patches that affect both physical consolation and passionate well-being. Opportune and exact determination is basic for viable treatment; however, it remains troublesome due to its likeness to other dermatological disorders. This research presents a Psoriasis Detection and Severity Classification Framework built on MobileNetV2, a lightweight and effective profound learning demonstrate custom fitted for real-time utilize in resource- constrained situations. Through a basic image-upload interface, healthcare suppliers or patients can yield scalp pictures for robotized investigation. The framework to begin with recognizes the nearness of psoriasis with 90% accuracy, at that point classifies its serious- ness as either “low” or “moderate to severe” with 87% accuracy. This two-step prepare conveys prompt and clinically profitable experiences, supporting more focused on and opportune care. Approved in a clinical setting, the demonstrate illustrates solid unwaver- ing quality and down-to-earth appropriateness. It decreases reliance on expert-driven diagnostics and quickens treatment choices. By coordination AI with restorative hone, this framework improves demonstrative accuracy, streamlines workflows, and engages clini-cians to convey speedier, more personalized care reshaping the scene of dermatological
Challenges and Practices Identification via Systematic Literature Review in the Design of Green/Energy-Efficient Embedded Real-Time Systems
Get PDFAs most embedded devices are portable, that is they are operated by batteries, early battery exhaustion is likely to cause the failure of the embedded real-time systems (ERTS). Therefore, developers and users enjoy the services of the ERTS but face green and energy consumption challenges. Studies show that attempting to design green ERTS may lead to some serious issues or deteriorate some of the quality characteristics of the embedded systems. Energy conservation in ERTS has continued to be an area of interest in the past years. Energy efficiency or certain quality features are considered while designing ERTS, but these two factors are not often considered together because they have direct impact on each other in ERTS. The purpose of this research is to identify the challenges in the design of green ERTS and the solutions that can be employed to address those challenges. A review of the relevant literature was conducted to define the problems and practices under consideration. Based on a comprehensive Systematic Literature Review (SLR), we have found 8 challenges and 34 practices from 65 papers in the green ERTS context. The results of our SLR will help us develop a framework for creating green ERTS in the future
Petrochemical Investigation of Secondary Mineralized Volcanogenic Massive Sulfide (VMS) and Supergene Enrichment Economic Deposits in Jandrey-Annar, Upper Dir, Pakistan
Get PDFThis research was about the petrographic and geochemical study of the secondary mineralized Volcanic Massive Sulfide (VMS) deposits of Uthror Volcanics at the Jandrey-Annar study area. Sample examination under the microscope indicates the presence of plagioclase feldspar, sericite, and secondary minerals, such as limonite, hematite, and malachite. Subhedral phenocrysts of pyrrhotite and a highly altered groundmass are indicative of post-magmatic hydrothermal alteration and feldspar sericitization. (Quartz in veins and vugs with undulose extinction indicates recrystallization. The secondary minerals formed by supergene processes were identified by the petrographic index as the products of oxidation and weathering processes of primary sulfide ores. Loss on Ignition (LOI) returns vary from 3.24% to 4.72%, verifying the presence of hydrous mineral species and carbonates, typical for mature secondary mineralized VMS deposits. The rocks are classified as tephrite-basanite, and trachybasalt based on geochemical analysis (AAS and XRF) with the following ranges in their concentrations: SiO₂ (45–48%), Al₂O₃ (16.02–18.63%), CuO (10.48–13.69%), and Fe₂O₃ (5.49–6.20%). The SiO₂ binary plots show positive trends for TiO₂, Al₂O₃, P₂O₅, and K₂O, and negative trends for Fe₂O₃, MgO, CaO, and Na₂O confirming fraction crystallization. High K₂O values indicate the high-K calc-alkaline series. The 10Mn-TiO₂-10P₂O₅ ternary plot classifies the rocks as oceanic island arc basalts, while the R1-R2 plot indicates a late orogenic environment. These results demonstrate mineralization associated with hydrothermal alteration and subduction-related magmatism. Based on analysis of variance (ANOVA) and t-test, high geochemical variation is represented by highly significant (p < 0.01) and significant (p < 0.05) enriched variables including CuO, Fe₂O₃, and MnO, with moderately varying SiO₂, TiO₂, Al₂O₃, and Na₂O, the results indicate hydrothermal alteration and episodic stages of secondary mineralization within the Uthror Volcanics. This high economic potential of the copper ore due to secondary mineralization and supergene enrichment processes has made the region an important target for mineral exploration
Exploring cGANs for Urdu Alphabets and Numerical System Generation
Get PDFUrdu ligatures play a crucial role in text representation and processing, especially in Urdu language applications. While extensive research has been conducted on handwritten characters in various languages, there is still a significant gap in studying raster-based generated images of Urdu characters. This paper presents a generative model designed to produce high-quality samples that closely resemble yet differ from existing datasets. Utilizing the power of Generative Adversarial Networks (GANs), the model is trained on a diverse dataset comprising 40 classes of Urdu alphabets and 20 classes of numerals (both modern and Arabic-style), with each class containing 1,000 augmented images to capture variations. The generator network creates synthetic Urdu character samples based on class conditions, while the discriminator network evaluates their similarity to real datasets. The model’s effectiveness is assessed using key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID). The results confirm that the proposed GAN-based approach achieves high fidelity and structural accuracy, making it highly valuable for applications in text digitization and Optical Character Recognition (OCR)