International Journal of Innovations in Science & Technology
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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
Gender-Based Analysis of Employee Attrition Prediction Using Machine Learning
Get PDFEmployee turnover is a significant problem in organizations because it comes with productivity and cost implications. This paper focuses on predicting employee turnover using machine learning techniques that incorporate gender aspects. We used strong Random Forest classifiers to predict attrition based on a wide cross-section of the employees’ activities and the feature importance assessment. The procedure involved data cleaning, splitting the dataset for males and females, creating models for them, and using assessment tests with different measures. When we separated the data base by gender, our analysis identified unique factors that predisposed the two groups to dropping out. The importance of features, the ROC curve, and the SHAP map showed how variables such as "job role," "monthly income," and "work-life balance" affected attrition differently between males and females. For female employees, job satisfaction and time directly influenced attrition, whereas for male employees, previous companies and distance from home had a greater impact. The results of the research therefore imply the need for gender-sensitive HR practices that can inform the development of gender-sensitive accommodation policies as a way of responding to the challenges facing each gender. This approach aids in the explanation of attrition tendencies and the provision of better organizational practices
Dynamic Malware Detection Using Effective Machine Learning Models with Feature Selection Techniques
Get PDFDynamic Malware is a type of virus that is self-modifying, which makes it difficult to analyze in the course of its operation. It occasionally changes its behavior based on the existing environment and the context of execution. The goal of this study was to identify and detect dynamic malware in Android devices using effective machine-learning models with feature selection techniques. With new malicious software emerging daily, relying solely on manual heuristic analysis has become ineffective. To address this limitation, the study used dynamic detection methods to detect the events of interest using machine learning models. Some of these measures entailed duplication of an environment in which the behavior of malware could be replicated and then come up with reports. The reports were then transformed into sparse vector models so that other machine-learning techniques could then be applied to them. In this research study seven different models, namely, KNN, DT, RF, AdaBoost, SGD, Extra Trees, and Gaussian NB, were used to train an effective malware detection model to predict the dynamic malware in its early stages. The study showed that Random Forest, Stochastic Gradient Descent, Extra Tree, and Gaussian Naive Bayes classifiers achieved the highest accuracy compared to other models. This research study endorses the application of machine learning-based automated behavior analysis for malware detection, about the complexities involved in the dynamic behavioral analysis of malicious software
Geo-visualization of Debris Flow Susceptibility in District Chitral, North-West of Pakistan
Get PDFDebris flows are a recurrent environmental hazard in hilly regions and significantly impact socioeconomic development in Pakistan. This study aims to conduct debris flow risk zonation using remote sensing data, including NASA\u27s Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) and Landsat-8 imagery. These data were combined with geographic indices to identify debris flow factors such as slope, aspect, elevation, vegetation cover, and land cover changes like NDWI and NDVI. The weighted overlay technique was employed to achieve the study\u27s objective in the target area. The classes were ranked from most to least favorable, with numerical weights assigned based on each factor\u27s importance in debris flow occurrence. A composite map was then developed using the weighted overlay analysis to represent the significance of each factor. The resulting debris flow risk zonation map categorized the area into four classes: very high-risk, high-risk, moderate-risk, and low-risk zones. The villages located in the very high-risk zone include Mulkoh, Mastuj, Reshun, Shegram, Terich Gol, Rogar, Asurat, Boni, Brep, and Rech Tockhow, which have been frequently affected by hazards over the past decade. While the results and landslide susceptibility maps provide valuable insights for understanding landslides and planning mitigation measures, field surveys are essential for more accurate predictions. Overall, the study offers important information for authorities to prioritize landslide mitigation efforts in the region
Synergizing Digital Twin Technology for Advanced Depression Categorization in Social-Media through Data Mining Analysis
Get PDFThe progression from negative emotions to depression is a significant concern, marked by persistent sadness and an inability to cope with challenging circumstances. Regrettably, it can lead to the extreme step of suicide. According to the World Health Organization (WHO), 4.4% of the global population currently grapples with depression. Shockingly, 700,000 individuals worldwide took their own lives in 2023, and this tragic number continues to escalate. Our objective is to detect signs of depression in individuals through their social media posts, SMS, or comments. We collected nearly 10,000 pieces of information from Twitter comments, Facebook posts, and remarks. Employing data mining and machine learning algorithms has proven instrumental in swiftly discerning individuals\u27 emotional states. To predict depression versus non-depression, we employed six classifiers, with support vector machines (SVMs) demonstrating the highest accuracy. A comparison between SVM and Naïve Bayes revealed that Naïve Bayes yielded superior results in our study
Ransomware Resilience: A Real-time Detection Framework using Kafka and Machine Learning
Get PDFRansomware has emerged as a prominent cyber threat in recent years, targeting numerous businesses. In response to the escalating frequency of attacks, organizations are increasingly seeking effective tools and strategies to mitigate the impact of ransomware incidents. This research addresses the pressing need for real-time detection of ransomware, offering a solution that leverages cutting-edge technologies. The surge in ransomware attacks poses a significant challenge to the cybersecurity landscape, compelling organizations to adopt proactive measures. Recognizing the urgency of the situation, this study motivates the exploration of an innovative approach to ransomware detection. By utilizing advanced tools such as Apache Kafka and Spark, we aim to enhance detection capabilities and contribute to the resilience of businesses against cyber threats. Our methodology employs the Kafka tool and Spark for real-time identification of ransomware exploits. The research utilizes the CIC-MalMem-2022 dataset to develop and validate the proposed model. The integration of Apache Kafka with traditional machine learning techniques is explored to improve the accuracy of cyber threat detection, offering a comprehensive and efficient solution. The implemented model exhibits a commendable detection rate of 95.2%, demonstrating its effectiveness in identifying ransomware attacks in real-time. The combination of Apache Kafka\u27s streaming capabilities and established machine learning methodologies proves to be a potent defense against the evolving landscape of cyber threats. In conclusion, our research provides a robust and practical approach to combating ransomware threats through real-time detection. By leveraging the synergy of Kafka and machine learning, organizations can fortify their cybersecurity defenses and respond proactively to potential ransomware exploits. This study contributes valuable insights and tools to the ongoing efforts in enhancing cyber resilience
A Systematic Review of Desertification Identification with Multispectral LANDSAT Image and Deep Learning Models
Get PDFThe use of multispectral Landsat images and deep learning models for desertification detection has been reviewed in this research. The role of deep learning models is found to significantly increase the identification accuracy of the researchers, complemented by the inclusion of Landsat imagery to capture key desertification indicators. The research reviews difficulties including geographical resolution, data variability, uncertainty, and validation, alongside different desertification identification methods, techniques, advancement, and limitations. The research also highlighted the necessity of historical data, data continuity, and data fusion, among other issues on data availability and quality. The research advocates for the combination of high-resolution photography, climate and weather data, and socioeconomic data for better desertification detection while the research has identified more complex deep learning architectures, better uncertainty estimation, explainability and interpretability improvement, and the integration of process-based models as potential areas of research. The research concludes by highlighting the importance of precise desertification identification in effective land administration and ecological preservation
An Artificial Intelligence Vision Transformer Model for Classification of Bacterial Colony
Get PDFThe application of AI and machine learning, particularly the vision transformer method, in bacterial detection presents a promising solution to overcome limitations of traditional methods, offering faster and more accurate detection of disease-causing bacteria like E. coli and salmonella in water, crucial for human survival, with ongoing research to further assess its effectiveness in microbiology. This research introduces a revolutionary positional self-attention transformer model for the classification of bacterial colonies. Leveraging the proven success of transformer architectures in various domains, we enhanced the model\u27s performance by integrating a positional self-attention mechanism. We presented a novel approach for bacterial colony classification utilizing a positional self-attention transformer model. This allows the model to effectively capture spatial relationships and patterns within bacterial colonies, contributing to highly accurate classification results. We trained the model on a substantial dataset of bacterial images, which ensures its robustness and generalization to diverse colony types. The proposed model adeptly captured the spatial relationships and sequential patterns inherent in bacterial colony images, allowing for more accurate and robust classification. The proposed model demonstrated remarkable performance, achieving an accuracy of 98.50% in the classification of bacterial colonies. This novel approach surpasses traditional methods by effectively capturing intricate spatial relationships within microbial structures, offering unprecedented accuracy in discerning subtle morphological variations. The model\u27s adaptability to diverse colony shapes and arrangements marks a significant advancement, promising to redefine the landscape of bacterial colony classification through the lens of state-of-the-art deep learning techniques. The high classification accuracy attained by the model, suggests its potential for practical applications in the early diagnosis of infectious diseases and the development of targeted treatments. The findings of this study underscore the effectiveness of incorporating positional self-attention in transformer models for image-based classification tasks, particularly in the domain of bacterial colony analysis
Cluster Analysis of COVID-19 Through Genome Sequences Using Python Bioinformatics Library
Get PDFIntroduction and Importance of Study:
During the COVID-19 pandemic, mortality rates varied across different regions of the world. To better understand the virus\u27s behavior, it\u27s important to gain in-depth knowledge of the nucleotide records of the COVID-19 genomic sequence.
Novelty Statement:
In the study, researchers analyzed clusters of highly affected countries to find similar codons in countries with a similar effect of the virus through Python based library.
Material And Method:
Nucleotide records were extracted from the NCBI database in FASTA format. Further Python Bioinformatics library was used to form the clusters of each country using the K-means clustering technique.
Result and Discussion:
The study focuses on finding the similarities between the codons of amino acids in different countries that are affected in a similar way during COVID-19. For instance, China and the EU have a lower mortality rate and have Leucine, Methionine, Isoleucine, and Valine amino acids in common. On the other hand, countries like Pakistan and India have Leucine, Isoleucine, Valine, and Threonine in common and an average death rate. Moreover, Brazil and the US have a higher mortality rate and share similar codons such as Leucine, Glutamine, and Amber.
Concluding Remarks:
The study shows that countries affected by COVID-19 in a similar way share some common amino acids and their respective codons
Fine-Tuning Audio Compression: Algorithmic Implementation and Performance Metrics
Get PDFIntroduction/Importance of Study:
This study introduces a comprehensive evaluation of audio compression algorithms to address the increasing demand for efficient data compression techniques in various audio processing applications.
Novelty statement:
Our research contributes novel insights into the comparative analysis of audio compression algorithms, offering a systematic approach to assess performance across multiple dimensions.
Material and Method:
The research methodology involved the selection of a diverse dataset comprising five audio files, rigorous implementation of four prominent compression algorithms, and systematic evaluation of performance metrics.
Results and Discussion:
The abstract primarily focuses on presenting the findings of the comparative analysis, highlighting the performance of MP3, LPC, Wavelet, and Sub band algorithms across various evaluation parameters.
Concluding Remarks:
In conclusion, our study identifies Wavelet compression as the optimal choice among the evaluated algorithms, offering exceptional accuracy, perceptual quality, and minimal distortion in audio compression