As a result of the harmful effects of GHG on the environment, and the increase in fossil fuel prices, the world is currently searching for sustainable alternative energy sources instead of fossil fuels. Bangladesh is a country that has an ambitious aim to rely on sustainable energy. This study discusses the viability of a 100MW PV power project in Rajshahi, Bangladesh by using RETScreen software. This includes benchmarking, emissions analysis, and financial analysis. The power generation cost of the proposed PV power plant is 0.09 $/kWh based on the benchmark assessment and the annual power provided to the national power grid is determined to be 140,155MWh. According to the emission analysis, overall perspective emissions to the surroundings will be decreased by 78,797.7 tCO2 annually. The NPV and cumulative cash flow demonstrate good results based on the financial analysis. The outcome is, the proposed project is both financially and technically feasible, as well as environmentally beneficial, and will assist the government in meeting the aim of developing clean and sustainable energy.

The wide use of supply chain management systems in various business sectors encouraged researchers and those who were concerned to explore and employ efficient technologies to improve such systems. The integration of blockchain into supply chains has proved its effectiveness at increasing the customer’s trust level, as well as many other features, such as traceability, immutability, provenance awareness, etc. Moreover, the use of private permissioned blockchain networks, for instance Hyperledger Fabric (HLF), not only leverages the level of confidence, but also increases the speed of transaction execution. In this paper, an exploratory detailed study on Hyperledger Fabric framework is conducted. The study focused on the HLF network design, the consensus algorithms used in HLF, the HLF smart contracts and the transaction flow stages. Moreover, a number of illustrative case studies that used HLF into their networks designed for food supply chain management systems have been introduced. The basic design components in each of the applications are reviewed as well as the main goals and desired outcomes.

Software evolution is the process of updating a software from it’s initial phase incorporating updates taken from the users. E-type software takes feedback and requirements from the users and updates the software accordingly. In the 70’s, Lehman proposed some laws of software evolution, which are still considered as the standards of measuring software evolution. Now-a-days, E-type softwares are getting very popular as users now use softwares by giving feedback. We are measuring Lehman’s laws of software evolution for two E-type softwares, Next.js and Angular. By downloading the source codes from github repositories, we analysed the source codes and evaluated if the softwares follow Lehman’s Laws of Software Evolution or not. This analysis completes the research which aims at measuring if the softwares evolve by maintaining Lehman’s Laws.

Breast cancer is a leading cause of death among women, and the subjectivity of human visual perception and lack of automated detection methods can lead to misclassification of breast cancer images. In this study, a breast cancer classification model using a Convolutional Neural Network (CNN) deep learning algorithm was proposed. The model demonstrated high accuracy in classifying breast images as benign or malignant, with a classification accuracy of 97.1%. The model was also able to run on low computational resources. The study used a dataset of 2009 breast images labeled by two radiologists and included six scenarios based on different hyperparameters, augmentation values, pretrained models, and models built from scratch. While the performance of the proposed model was promising, further improvement may be achieved by using a larger breast image dataset and a machine with more powerful GPU hardware.

This study explores the conversion of English to Hindi, first to text, and subsequently to speech. The first part of the implementation is the text recognition from images, in which two approaches are used for text character recognition: a maximally stable extensible region (MSER) and grayscale conversion the second part of the paper deals with the geometric filtering in combination with stroke width transform (SWT). Subsequently, letter/alphabets are grouped to detect text sequences, which are then fragmented into words. Finally, a 96 percent accurate spell check is performed using naive Bayes and decision tree algorithms, followed by the use of optical character recognition (OCR) to digitize. The word Give our text-to-speech synthesizer (TTS) the recognized text to convert it to Hindi language using the text-to-speech model. Based on aspects such speech speed, sound quality, pronunciation, and clarity.