http://dspace.ewubd.edu:8080/handle/123456789/12 2026-04-04T11:07:58Z 2026-04-04T11:07:58Z Ahmed, Towsif Poly, Irean Hossain Mim, Mohsina Zaman Faruk, Md. Omar http://dspace.ewubd.edu:8080/handle/123456789/4342 2024-04-22T03:14:54Z 2024-04-18T00:00:00Z

Suicide & Depression Detection Using Machine Learning Ahmed, Towsif; Poly, Irean Hossain; Mim, Mohsina Zaman; Faruk, Md. Omar Nowadays, depression is the most common mental-health issue and the leading cause of suicide and self-injurious behavior. Due to social stigma and lack of knowledge, clinical diagnosis of various mental health conditions is costly and often disregarded. These days, people choose to communicate using online social media platforms to convey their ideas, sentiments, and emotions. As a result, non-clinical mental health screening and surveillance can be facilitated by using user-generated information from online social media platforms. For quite some time, social media data has been utilized to automatically identify mental health problems through the use of traditional machine learning and natural language processing approaches. And the goal of our research is to examine how Machine Learning methods may be applied to the early detection and non-clinical, predictive diagnosis. To the best of our knowledge, we did not find any systematic literature review that studies the applications of machine learning techniques in this domain. In order to address this research gap, we conducted a systematic literature review relevant research studies published until date that have applied machine learning techniques for detecting depression and suicide or self-harm behavior from social media content. Our work comprehensively covers state-of-the-art WRT. techniques, features, datasets, and performance metrics, which can be of great value to researchers. We enumerate all the available datasets in this domain and discuss their characteristics. We also discuss the research gaps, challenges, and future research directions for advancing & catalyzing research in this domain. To the best of our knowledge, our study is the only and the most recent survey for this domain covering the latest research published until date. Based on our learnings from this review, we have also proposed a framework for mental health surveillance. We believe the findings of our work will be beneficial for researchers working in this domain. This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh

2024-04-18T00:00:00Z Islam, Eva Moon, Marzana Rahman Vasha, Tasnim Karim Mahdi, Md. Tasean http://dspace.ewubd.edu:8080/handle/123456789/4248 2024-02-05T08:00:11Z 2023-11-28T00:00:00Z

Unmasking Deception: Analyzing Fake Product Reviews through Machine and Deep Learning Islam, Eva; Moon, Marzana Rahman; Vasha, Tasnim Karim; Mahdi, Md. Tasean Online product evaluations have become a vital resource for shoppers looking for knowledge and direction for their purchases in the age of digital commerce. The prevalence of fraudulent product evaluations has jeopardized this priceless source of knowledge, seriously compromising the trustworthiness and integrity of online review systems. By deeply examining the identification and analysis of phony product evaluations and utilizing the capabilities of machine and deep learning algorithms, this thesis aims to address this problem. This research project starts by gathering a wide range of product reviews from different online sources. We make sure the data is consistent and of good quality by using rigorous preprocessing. We then extract important features from the reviews, like sentiment, language, and metadata, to help us analyze them. We use regular machine learning models to figure out if the reviews are real or fake, which helps us measure performance. Then, we use deep learning techniques like CNNs to get into the details of the text, which helps us detect fake reviews more accurately. This study also emphasizes the interpretability and explainability of model predictions, offering insight into the variables influencing the detection of false reviews. Our algorithms are applied to real-world datasets and settings, proving their efficacy in identifying fraudulent product evaluations across a variety of sectors, allowing us to evaluate the practical value of our research. In addition to algorithmic skill, ethical issues relating to privacy and fairness in fake review analysis are thoughtfully addressed, ensuring that the creation and application of these models are in line with responsible AI practices. To sum up, this thesis helps with continuous efforts to protect the integrity of online review systems, allowing customers to make wise decisions, and upholding the reliability of e-commerce platforms. By exploring the complex world of bogus products, This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh

2023-11-28T00:00:00Z Muttaque, Ahanaf http://dspace.ewubd.edu:8080/handle/123456789/3950 2023-03-23T04:53:42Z 2022-10-30T00:00:00Z

Report on RF Mesh System for Smart Metering Muttaque, Ahanaf This paper describes the RF mesh system design and ongoing project in DPDC for advance metering infrastructure and its performance. This system is based on Neighborhood Area Lan (LAN). Its operating band is 920-925 MHz and is based on the frequency hopping spread spectrum. The deployment scenario's geographic model serves as the foundation for the performance evaluation, which uses geographical routing and the appropriate models for radio transmission. It ensures accurate and error free billing. Further, we conclude that the RF mesh system supports us for smart metering and create transparency to customers and DPDC. This internship report submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electronics and Telecommunication Engineering of East West University, Dhaka, Bangladesh

2022-10-30T00:00:00Z Jahangir, Maimanah Binte http://dspace.ewubd.edu:8080/handle/123456789/3949 2023-03-23T05:48:09Z 2023-01-18T00:00:00Z

Report on Power Line Communication System for Smart Metering Networks Jahangir, Maimanah Binte Power line communications (PLC) are estimated using a number of technical research papers and up-to-date journals. The purpose of this study is to pinpoint crucial power line communications components for indoor and outdoor applications as well as the usage of consumer communication during the global commercial development of numerous potential services. Considerations include potential services, bandwidth, service area, quality, dependability, and pricing. The generation side of the power grid has seen major change recently, with a rise in the usage of energy sources (renewable), which are less centralized and have more variable availability than traditional ones. Similar to how electric vehicle uptake would significantly alter consumption habits. Because of these changing circumstances, the power grid’s assets need to be better monitored and controlled, and smart metering is essential to achieving these goals. Power line communications (PLC) has emerged as a practical alternative in many situations, despite the fact that there are other communication technologies available for smart metering applications. Additionally, it offers a private communication network to the distribution system operator (DSO) and seamlessly integrates sensing and communication functions. This paper provides a brief explanation of PLC in the context of smart meters and the necessity of power line communication. This internship report submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Electronics and Telecommunication Engineering of East West University, Dhaka, Bangladesh

2023-01-18T00:00:00Z