Impact of Consumer Price Index (CPI) and Inflation on GDP: Evidence from Bangladesh

The study examines the impact of the Consumer Price Index (CPI) and Inflation on the Gross Domestic Product (GDP) of Bangladesh during 2006-2007 to 2021-2022. The research investigates the connection between GDP and these economic indicators using multiple linear regression analysis. The results reveals that the CPI significantly boosts GDP, suggesting that shifts in consumer prices over time have a big impact on the nation's overall economic development. In contrast, there appears to be a modest negative correlation between the GDP and the inflation rate. The study emphasizes the CPI's crucial influence on Bangladesh's GDP and highlights its significance as a major factor influencing the country's economic growth. These findings have important ramifications for stakeholders and policymakers, emphasizing the necessity of taking calculated risks to control inflation and the CPI in order to promote Bangladesh's sustainable economic progress and enhance excellence of life for its populace

Use of Machine Learning Application for Business Perspective

Customer segmentation plays a crucial role in understanding customer behaviour and tailoring marketing strategies. This project focuses on using K-Means clustering, a popular unsupervised machine learning algorithm, for customer classification based on their purchasing behaviour. The objective is to develop a customer segmentation model that can effectively group customers into distinct clusters to facilitate targeted marketing efforts. The project begins with the collection of a fictitious e-commerce dataset consisting of 5000 customers with their purchase history. The dataset includes features such as customer ID, age, gender, annual income, and spending score. Data preprocessing techniques are applied to handle missing values and standardize the data, ensuring accurate and meaningful analysis. Feature extraction involves selecting relevant features from the dataset, including age, gender, annual income, and spending score. These features provide valuable insights into customer behaviour and serve as the basis for customer segmentation. The K-Means clustering algorithm is employed to classify customers into distinct clusters based on their purchasing behavior. The algorithm partitions the customers into K clusters by minimizing the sum of squared distances between the customers and their respective cluster centers. The optimal value of K is determined using the elbow method, a visual technique that identifies the point of maximum curvature in the sum of squared distances plot. The effectiveness of the K-Means clustering model is evaluated using the Silhouette score. This score measures how well each customer fits into its assigned cluster, with values ranging from -1 to 1. A higher Silhouette score indicates better cluster cohesion and separatio

Comparative Evaluation of Predictive Models on Kidney, Lung Cancer and Heart Disease

This study supports advances in machine learning to improve early detection and treatment planning for lung cancer, cardiovascular disease, and kidney disease. We compare traditional models such as decision trees and logistic regression with complex techniques such as support vector machines, random forests, and KNN and evaluate them on publicly available data. This hybrid approach uses random forest and decision tree classifiers, leveraging adaptive learning to improve model accuracy. Results showed high prediction accuracy for kidney disease and lung cancer , while prediction accuracy for heart disease was average . This difference indicates the need for better work and more information. Future studies will focus on improving cardiovascular models, addressing data uncertainty, and integrating predictive models into clinical practice to support early diagnosis and personalized treatment to improve patient outcomes. This study demonstrates the potential for machine learning to have a major impact on diagnosis and patient management

Vibration-Based Condition Monitoring of Shaft Bearing Systems Using Machine Learning Techniques

A shaft-bearing system is an essential part of rotating machinery. To guarantee that a shaft bearing system operates safely and reliably, the bearings' condition must be monitored on a regular basis. Bearing and shaft failures are thought to be the leading reasons of failure in various revolving machines used in the industry at highre and lower speeds. The condition of the bearing changes throughout use, so do the vibrations, and their characteristics vary depending on the reason. As a result, the bearing's unique property makes it suited for vibration monitoring and other procedures. The vibration measurement approach may reliably anticipate the upcoming failure and life of a mechanism or component based on changes in vibration signals.  As a result, the bearing's unique property makes it suited for vibration monitoring and other procedures. The vibration measurement approach may reliably anticipate the future failure and life of a machine or component based on changes in vibration signals. As a result, the goal is to extend the machine's life by detecting faults early on, allowing for an effective maintenance program to be implemented to remedy the problem. Subsequently, this research uses machine learning methods to detect bearing problems, compare them to various faulty and standard models, and categorize the bearing type. In this research work, we use outer race fault data from the Bearing data set to extract the time domain features from the dataset using Various machine learning models, including Principal Component Analysis, K-NEAREST NEIGHBOURS (K-NN), SUPPORT VECTOR MACHINES (SVM), RANDOM FOREST CLASSIFIER, DESICION TREE, and LOGISTIC REGRESSION. As a consequence, we obtain the best model that performs optimally on the data set. Finally, the proposed methods of condition monitoring will be implemented in a real-world case study of the shaft bearing system. Thus, vibration testing is used to monitor the state of the shaft bearing system, allowing for the identification of problematic bearings and improved performance after they are replaced

Semantic Coherence and NLP: Redesigning post-COVID Mental Health Diagnostics with CNNs and LSTMs

The COVID-19 pandemic has intensified the need for innovative, scalable diagnostic tools for mental health, given the surge in related disorders globally. This study presents a novel neural symbolic approach leveraging natural language processing (NLP) to analyze semantic coherence in text data, aimed at predicting mental health outcomes. Integrating convolutional neural networks (CNNs) with long short-term memory networks (LSTM) and an attention mechanism, this model excels in extracting and emphasizing critical linguistic features from vast datasets of online textual communications. Our evaluations show that the model achieves an accuracy of 92.4%, with precision, recall, and an F1-score significantly superior to traditional LSTM models. The ROC-AUC score of 0.92 highlights its effectiveness at distinguishing various mental health states, while the attention mechanism enhances the model’s interpretability, shedding light on key text features indicative of mental distress. This research underscores the potential of AI in enhancing mental health diagnostics in the context of current events, proposing a powerful tool for early detection and intervention

Efficient Sunflower Solar Power Tracking and Monitoring System

The amplified need for renewable energy sources has increased the demand for efficient solar energy systems. This paper brings forth an inspiration of a sunflower-solar power tracking and monitoring system. In this approach, the optimum capturing of energy has been achieved by tracking the movement of a natural sunflower as it follows the movement of the sun. It consists of miniature solar panels, N2O gear motors, Li-Po batteries, MG 996 R servo motors, limit switches, light-dependent resistors (LDRs), and an Arduino Nano 328P microcontroller, integrated along with an L293D motor driver. Integration of proactive sensing and real-time tracking capabilities into the proposed system heavily improves the generation of solar energy, thus significantly reducing energy wastage. Experimental results confirm that this new design is effective and promising in improving the efficiency of solar energy

A Comprehensive study on Satellite Image Super-resolution using Diffusion and GAN based model

Object detection and feature extraction from satellite images is a crucial step while using satellite images for purposes like navigation, urban planning, weather monitoring, etc. While deep learning approaches are too common for object detection task, but when the satellite images are of low quality, the small objects are missed by detection model due to their size and visibility issue.  In this paper we propose a study of two broad areas of Generative AI models namely GANs and Diffusion model and their ability to handle the low-resolution images to improve overall detection problem. We train SRGAN and Diffusion based super-resolution model on custom real-time datasets and present a comprehensive performance evaluation and analysis.  We found that Diffusion model increased the object detection rate by almost 130% when compared with Raw image object detection

High Performance Approximate Multiplier using reversible logic gates

Reversible logic has previously been shown to cause higher power consumption and a significant amount of dissipated energy because of information loss in standard design methods. This project describes the approximate multiplier using Reversible logic gates. In this design, the reversible logic gates replace the half adder and full adders in the multiplier. It uses two RG(Reversible Gate) in place of single reversible gate. So that it reduces the garbage value produced, which helps to decrease the overall delay and power consumption. The proposed Approximate Multiplier uses the product’s least significant half as a constant compensation term and the remaining half is precisely calculated. This can be a effective alternative for exact multipliers in practical error-resilient applications and Digital Image Processing

CONNECTING THE DOTS: LINKING CORONARY DISEASES WITH COVID-19 PATIENTS THROUGH SUPPORT VECTOR MACHINE ALGORITHM

The COVID-19 pandemic has left a lasting impact on global health, with a significant portion of survivors experiencing persistent health effects termed as ‘LONG COVID’ or ‘POST COVID-19 SYNDROME’. In this research, we propose a novel approach utilizing Support Vector Machine (SVM) algorithm to analyse patient data and predict the multifaceted nature of post-COVID-19 Syndrome, particularly focusing on the interlinkage of coronary diseases with COVID-19 patients. Our methodology involves collecting and analysing extensive patient data, including pre-conditions and post-conditions of COVID-19, to identify patterns and associations between various health issues. By leveraging the high-dimensional capabilities of SVM, we aim to provide accurate predictions and insights into the long-term health complications of COVID-19 survivors, thereby contributing to a better understanding of this critical area of healthcare. This approach stands out due to its ability to handle nonlinear relationships, noise in data, and large datasets effectively, offering valuable insights for healthcare professionals in managing post-COVID-19 complications

A DEEP AND MACHINE LEARNING COMPARATIVE APPROACH FOR NETWORKS INTRUSION DETECTION

Intrusion detection is intergral section of firewalls and other attacks prevention applications that works side by side with the attack pouncing section. The strongest attack prevention application is that of wide range of attack pouncing capability. Recently, data driven models are used for this task which offers the required capability of multiple type of attack detection. In this paper, foucse given to establish an attack detection system that compatible with various datasets and able to draw similar perfromacne in attack flection. Multilayer perception (MLP), Convolutional neural network (CNN). Machine learning algorithms are also deployed such as Random Forest (RF) and Boosting algorithms such as XGBoost, AdaBoost and CatBoost. The MLP algorithm was realized with best intrusion detection performance, it yielded a higher accuracy in both dataset cases. Overall, the classification results on the UNSW-NB15 dataset suggest that machine learning algorithms can be successfully applied to network intrusion detection tasks, with various algorithms demonstrating high levels of accuracy in distinguishing between normal and malicious network traffic