International Journal of Innovations in Science & Technology
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Cybersecurity Legislature Challenges and Remedies
Get PDFThe use of the Internet is growing rapidly in every field of life, such as in business, education, entertainment, information technology, the government sector, and sports. The majority of people are using internet services for online businesses and other online activities. Therefore, it is the need of the hour that the online system should be secure enough, and everyone is fully assured about privacy and the protection of their information. A country needs to have an efficient plan to secure its digital information. Different countries have established legislatures to manage cybercrime activities and cyber threats. In this paper, we analyzed the challenges faced by cybersecurity concerning legislation along with their probable solutions. The purpose of this paper is to provide an extensive review of the literature on cybersecurity, including its loopholes, and present the findings of a survey conducted in various organizations regarding cybersecurity. This study also highlights the improvements and the need for future work in the field of cybersecurity. In addition, the mandatory procedures and mitigation techniques to reduce the occurrence of cybercrime have also been discussed
Deep Recurrent Neural Network-Based Forecasting of Electricity Consumption and Anomalies Detection
Get PDFThe construction industry is among the greatest fuel consumers of the world and a major source of carbon dioxide. Owing to this, the environmental effect can be minimized when focusing on conserving energy in buildings. Misuse of energy in the effort of equipment and errors of humans in the work is never considered budget. The use of smart buildings will address this problem since it will track the use of energy, detect abnormal behavior, and remind the managers that they are supposed to take energy-conservation actions. The current paper considers the issue of anomaly detection in the hourly electricity consumption level on a real basis and gives a two-step process with a Long Short-Term Memory (LSTM) network. In the first step, there will be forecasting of energy consumption, and, following this, the anomalies will be identified with the assistance of an LSTM Autoencoder. The article draws comparisons between highly complex time-dependent feature extraction algorithms like Rough Autoencoder (RAE), Deep Temporal Dictionary Learning (DTDL). The other algorithms could not perform better than the proposed method, the range of R-squared value was 95.11, MAE was 38.5, the MSE was 2448.94, and the RMSE was 49.49. Besides, the paper evaluates the means through which the AI-based anomaly detection solutions can provide forecasts of the electricity consumption, and the LSTM networks and autoencoders were tested to be more appropriate in forecasting the electricity consumption than the other deep learning algorithms
Python and GLO-DEM Pixel-Based Hypsometry in Upper Indus Basin
Get PDFNorthern Pakistan’s Hunza and Shyok headwaters, where the Karakoram, Himalaya, and Hindukush ranges converge, host some of the largest mid-latitude valley glaciers outside the polar regions and play a decisive role in runoff and hazards of the Upper Indus Basin. Hypsometry provides a rapid, terrain-based approach to assess basin condition in such high mountain settings. In this study, a 30 m digital elevation model was used to delineate 31 sub-catchments, and for each unit, the hypsometric curve and hypsometric integral (HI) were derived. Methods were kept consistent across scales, with HI also recalculated on 1-4 km grid tiles, and spatial organisation tested through Global Moran’s I and the Getis-Ord Gi* statistic. Results reveal coherent belts of high HI aligned with the Main Karakoram Thrust, the Main Mantle Thrust, and the Karakoram Fault, indicating actively rising terrain and focused incision. Lower HI corridors occur in wider valley floors and recent fills, reflecting more mature landscapes and enhanced storage. HI distributions remain stable across tile sizes with mean values below one-half, while significant clustering confirms that these belts are intrinsic terrain signals. Harder crystalline and intrusive lithologies show higher HI on average, though wide variance suggests the combined influence of structure, rock strength, and relief. These geomorphic patterns carry direct hydrological meaning: high-HI belts imply fast translation of snow and ice melt with sharper seasonal peaks, whereas low-HI corridors favour storage and delay. Hypsometry, therefore, offers a cost-effective and reproducible tool for identifying active belts and providing priors for hydrological modelling and hazard planning in the Upper Indus Basin
An Innovative Machine Learning (ML) Approach in Fabric Defect Detection and Quality Assurance
Get PDFThe garment and textile industries are essential sectors that significantly contribute to a nation\u27s economic development. Fabric defect detection is a complex problem in the textile and technology industries since the efficacy and efficiency of automatic defect detection determine the quality and cost of any textile product. In the past, the textile industry used manual labor to find flaws in the fabric production process. The primary disadvantages of the manual fabric flaw identification technique are human weariness, lack of focus, and time consumption. This article introduces an innovative automated system for detecting garment defects powered by machine learning to revolutionize the traditional system and replace the manual inspection system. This innovative advanced system is trained and assessed using the 500-image dataset from the Artistic Milliners Company in Pakistan. The machine learning algorithm and image processing techniques form the foundation of AI technology, offering the best flaw detection accuracy. This work presents an automated fabric defect detection system driven by a supervised machine learning algorithm, i.e., SVM, that can accurately and precisely detect "hole" and "stain" faults. The system achieves a 72% precision and 74% recall for holes and an 85% precision and 83% recall for stains by utilizing a machine learning algorithm, i.e., SVM. The proposed method throws up vital issues like scalability and fabric sort flexibility. Compared to traditional manual processes, this new method lowers inspection costs by 65%, increasing productivity and setting a standard for automated and sustainable textile quality monitoring
A Robust Deep Learning Model for Early Glaucoma Detection Using Retinal Imaging
Get PDFThe Glaucoma Detection System is developed in such a way that it can enable early diagnosis of glaucoma by incorporating the latest technology with the patient-centric healthcare paradigm. It uses a user-friendly interface written in the Tkinter language and a Convolutional Neural Network (CNN) model, and is mostly useful in processing medical images. The purpose of the methodology is to democratize ocular care, focus on the insidious nature of glaucoma, and emphasize the need to have a highly accurate CNN model to detect the disease at the earliest stage. The key features are preset structures and real-time image processing, which will speed up detection and allow healthcare professionals to prioritize severe cases. The system encourages the development of multimodal integration and feedback of data in order to promote efficacy, proactive eye health, as well as the principles of fair access to care
Analysis of Web Application Security: Integrating WAF and SSL/TLS for Enhanced CMS Protection
Get PDFContent Management Systems (CMS) such as WordPress, Joomla, and Drupal power a significant portion of the web, making them prime targets for cyber threats, including TLS downgrade attacks, SQL injection (SQLi), cross-site scripting (XSS), and brute force attempts. Traditional security mechanisms often fail to mitigate these sophisticated attacks, leading to data breaches and unauthorized access. This research implements a multi-layered security framework integrating Web Application Firewalls (WAFs), TLS 1.3 enforcement, AI-driven vulnerability detection, and enhanced security headers on a WordPress test environment. Security audits using OWASP ZAP, Nessus, and Burp Suite validated the effectiveness of each component. Results demonstrate an 80% reduction in brute force attacks, a 93% decrease in SQL injection attempts, and a 100% elimination of XSS vulnerabilities. The implementation of WAF filtering, real-time monitoring, and strict access controls significantly reduced the attack surface. This study provides a scalable, adaptive security model capable of evolving with emerging cybersecurity challenges, offering a vital contribution to web application security
Unsupervised Detection of Credential Stuffing and Account Takeover Attempts through User Behavioral Biometrics in Web Applications
Get PDFCredential stuffing and account takeover (ATO) attacks can still be stated as being one of the most ongoing risks of contemporary web applications, as they use reused credentials and defy traditional authentication procedures. Conventional supervised detection approaches rely on labeled attack data (which is frequently non-existent or incomplete) in the real world. In this paper, a credential stuffing and ATO adversarial behavior multi-layer detector is suggested through modeling user behavioral biometrics based on the web-session navigation patterns and chaining information during the login procedure. The framework combines Isolation Forest and Local Outlier Factor to train a normal behavioral distribution and detect deviations that suggest abnormal use of the accounts, which are either automated or reports of hacked accounts. Analysis of three different datasets RBA, MSNBC, and CERT Insider Threat, has shown that the framework is highly detection sensitive with an ROC-AUC of up to 0.936 and an F1-score of 0.842 in login-level anomaly detection, and cross-domain generalization on enterprise data. The findings validate the practicability of unsupervised behavioral modelling as a lightweight defense mechanism that is scalable and resistant to credential abuse. The suggested solution ensures that labelling is reduced, the privacy of users is maintained, and a scalable basic infrastructure is provided to accommodate the adaptive and risk-centric authentication models in massive web applications
Microbial-Plant-Biochar System for the Removal of Pollutants from Effluents and Contaminated Soil
Get PDFIndustrial activities released wastewater containing heavy metals and synthetic dyes that remain in the environment for a longer period. These pollutants disrupt ecosystems, pose risks to human health, and continue to accumulate if they are not treated properly. Many conventional remediation methods are costly and often generate secondary waste, which limits their practical use. As a result, researchers are increasingly exploring sustainable and environmentally friendly alternatives. This review discusses an integrated-microbial-biochar system as a promising approach for wastewater and soil remediation. Biochar produced from materials such as sewage sludge, oil-field drilling mud, and agricultural residues offers a highly porous and chemically active surface that can effectively bind heavy metals (Cd, Cu, and Zn) as well as a wide range of industrial dyes. In addition, microbial strains such as Acinetobacter sp. and Bacillus subtilis play an important role in degrading organic pollutants, restoring enzymatic activity and contaminated soils, and improving nutrient cycling. Recent developments, including biochar-microbe beads and composite bioreactor systems, have shown better performance than biochar or microbes used alone. These combined systems enhance microbial survival, reduce toxicity, and significantly improve pollutant removal efficiency. By summarizing recent findings on pyrolysis conditions, microbial immobilization techniques, and pollutant removal behavior, this review highlights the potential of hybrid remediation strategies. Emerging modifications, such as magnetic chitosan-modified biochar, are also discuss future directions to further strengthen integrated remediation systems.
Position Prediction and Talent Discovery in Football Leagues Using Performance Data
Get PDFFootball has always been dependent on the subjective evaluation of scouts and coaches to find and hire players. Although these methods work to some extent, they usually have restrictions due to human biases, irregularity, and the huge volume of football data. As more data on player performance is made available, data analytics and machine learning represent a chance to introduce objectivity, consistency, and scalability in the recruitment process. This research study suggests a machine learning-based classification model along with a clustering model to classify football players in their main positional roles using statistical performance features. The research is based on the development of models that would help to differentiate among defenders, midfielders, and attackers based on their passing efficiency, contributions to defense, won duels, and attacking indicators. For data extraction, Fbref has been used as the source of data. The player-level data of the 2023-24 season of the Top 5 European Leagues has been extracted using the Python programming language. The data involved various statistical categories addressing all the areas of performance. Position labels were merged with the scraped tables to ensure accurate role mapping. This combination resulted in the creation of an entire dataset with both performance and position features. The dataset was cleaned and prepared using data preprocessing techniques, and selected features were then used in the training process. K-Means Clustering was applied to the PCA-transformed data to cluster similar players based on their playing profile. Different supervised learning algorithms have also been applied, such as Logistic Regression, Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Voting Classifier. The standard evaluation parameters are used to provide a detailed evaluation of the predictive performance. It was found that ensemble algorithms, in particular Random Forest and the Voting Classifier, performed better than single baseline models and were stronger and more reliable in positional classification. The results suggest the potential of machine learning models when recruiting players in football teams and to facilitate and aid expert judgment. This research sets up a systematic, data-driven framework that helps clubs to screen the enormous number of players effectively in a non-subjective manner
Effects of Exogenous Calcium and Magnesium on Physio-Hormonal Attributes of Trigonella Foenum-Graecum l. Under Polyethylene Glycol (Peg) Induce Drought Stress
Get PDFDrought stress is one of the abiotic stresses that adversely affect the plant growth parameters and physio-hormonal attributes. In the current work, we study the adverse effects of induced PEG drought stress in Trigonella foenum-graecum L. in the presence of calcium and magnesium concentration. The experiment was conducted in the botanical garden of Abdul Wali Khan University Mardan in a completely random design. There are eight treatments and one control having each of the trees replicated. The nutrients of calcium and magnesium ratio (4, 2, and 0.18) were added to the plant after 30 days adding the polyethylene glycol of concentration of (0.6 Mpa and 0.2 MPa) for 8 days. The results show that drought stress induced by PEG had a significant effect on the growth and physio-hormonal indices of the plant. It was found that calcium and magnesium both reduce the adverse effects of polyethylene glycol. All treatments helped ascorbic acid, salicylic acid, and auxins to give plant possible growth and development in due time reducing the effects of PEG. Similarly, in enzymatic activities, the maximum lipid peroxidase contents at p >0.05 are found in calcium and magnesium ratio 0.18 and polyethylene glycol 0.2 Mpa. The maximum ascorbic acid peroxidase was found at p>0.05 in Ca/Mg ratio 4. It is concluded from the study that the calcium and magnesium ratio mitigated the adverse effects of polyethylene glycol on Trigonella foenum-graecum L. growth by promoting hormones and enzymatic activities under PEG-induced drought stress