Journal of Informatics Electrical and Electronics Engineering (JIEEE)

Domestic Wastewater Treatment Using Natural Filtration and Solar Distillation Processes

Mahima Prajapati — 2024-12-31

Fresh water is most important daily requirement for human survival. On one hand many regions/countries are facing challenges related to availability of potable water for drinking and daily needs, on other hand water is wasted daily from different domestic applications. Processing and reuse of domestic waste water can help bridge gap between demand and supply. Present study describes a domestic waste water treatment process using combined natural filtration and solar distillation methods. In natural filtration process husk fibre, activated carbon and coconut husk fibre were used as filters. For solar distillation a pyramid shaped distillation unit is used. The samples of domestic waste water were collected from laundry, kitchen and house cleaning applications. The pH, TDS (total dissolved solids) and DO (dissolved oxygen) values of domestic waste water samples and water after treatment were tested. The results after treatment reveal that the values of TDS, pH, and dissolved oxygen are close to the standard values required for potable water which can be further used in different applications.

Refining Color Scheme Generation: Iterative K-Means Clustering and ARI Evaluation

Abhinandan Yadav — 2024-12-31

Color goes beyond mere visual sensation, holding profound sway over emotions, thoughts, and perceptions. It communicates, evokes moods, and significantly influences judgments. Research underscores its importance, with up to 90% of product assessments being based solely on color, highlighting its pivotal role in crafting memorable experiences and defining brand identities. The fusion of art and technology presents a captivating synergy within the realm of image-derived color schemes. Color palette generation from images is pivotal in graphic design, interior decoration, and digital media. This study delves into methodologies for extracting dominant colors from images and generating cohesive color schemes. Leveraging K-Means clustering with the Within-Cluster Sum of Squares (WCSS) method, we showcase superior performance compared to traditional approaches. The evaluation of palette coherence using the Adjusted Rand Index (ARI) facilitates consistency within the generated color schemes. Integrating methodologies with design tools and advanced color harmonies opens avenues for further innovation and customization. This study underscores the transformative potential of image-based color scheme generation, bridging the gap between computational analysis and creative expression. Through the convergence of artistry and technological prowess, we aim to enhance the design landscape and enrich user experiences across various applications and industries.

Multiple Disease Prediction System using Machine Learning

Ahad Rehman — 2024-12-31

Machine learning advancements have spurred a revolution in healthcare by making it possible to create prediction models for early disease diagnosis. This report introduces the Multiple Disease Prediction System (MDPS), a state-of-the-art approach that uses machine learning to forecast the likelihood of several diseases based on patient data, including medical history, lifestyle, and demographics. The MDPS addresses the growing difficulties in healthcare by focusing on the early detection of multiple diseases. Some of its crucial components include data preparation, feature selection, model training and disease Detection. Despite advantages like early detection and cost savings, dealing with data privacy, model interpretability, and continuous improvements is essential for MDPS's ethical and efficient usage in healthcare. As a result, MDPS has a significant potential to enhance public health and minimize the difficulties associated with chronic illnesses.

Sentiment Classification on Mobile Review Using Extraction of Sentiment Conveying Sentences

Mohammad Irsad — 2024-12-31

In the present time, sentiment analysis has become the most successful technique to identifying people's views, opinions or emotions about any product, service or event. Sentiment analysis became more popular as a result of the widespread use of e-commerce sites and social media platforms such as Twitter, Facebook etc. by individuals who want to express their feelings, views emotions or opinions about any product, service or event. Individuals make efforts in order to express themselves. Sentiment analysis is extremely helpful for companies that are selling a product to determine how their product was perceived by consumers. Therefore, it has become essential to generate fast, reliable and efficient techniques for mining user reviews. In this paper, we have proposed an approach to extract sentiment conveying sentences from the review and used three machine learning classifiers: Random Forest, Mul-tinomial Naïve Bayes and Random Forest. The experimental results show that the machine learning classifiers achieve higher accuracy and Random Forest achieve highest accuracy.

An In-Depth Evaluation of Recommendation Systems: Methods, Challenges, and Solutions

Pranjal Kumar Singh — 2024-12-31

Recommendation systems (RS) play a vital role in the digital landscape, in shaping user experiences across various platforms. It delves into the origins and key characteristics of Content-Based Filtering and Collaborative Filtering, backed by empirical analysis to underscore their practical significance. It will go through the intricate development stages of RS, spanning from data investigation to prediction methodologies, and tackles challenges such as the cold-start problem. RS is categorized into three main types: collaborative filtering, content-based filtering, and hybrid recommendation systems, highlighting their potential synergy in enhancing recommendation accuracy result and breadth. These insights lay the groundwork for subsequent, which explore evaluation techniques, seminal research, dataset analysis, and experimental findings, concluding with reflections and avenues for future research to advance the field of recommendation systems.