A global consumption of energy is primarily met by the renewable and non-renewable energy production resources. It is necessary to understand the pattern of global energy consumption in past to refine the overall energy policy for an upcoming demand of the energy market. The consumption of energy and its insights are helpful for grid management and forecasting. This paper presents the consumption of renewable and non-renewable energy resources by different nations and presents the analysis of the impact of COVID19 pandemic over the consumption of Energy. From the detailed analysis in this study, it is evident that all countries are shifting their interest to use renewable sources of energy generation. The global consumption of energy was constantly increasing up to 4% each year for three decades (1990 to 2020). However, during COVID-19 outbreak, energy consumption shows a downward trend in 2020 to -4%, which is twice lower than the decrement of energy consumption observed 2008-2009 economic crisis. The COVID-19 pandemic has seriously affected energy consumption of all countries in the world.

An Enterprise Resource Planning (ERP) system is a software application that serves as a centralized platform to streamline and automate organizational functions and share real-time data, facilitating efficient communication and collaboration. It provides an all-inclusive approach to managing and optimizing business processes, boosting efficiency, fostering cooperation, and giving an overall picture of how the organization is operating. However, the traditional centralized databases in ERP systems pose security concerns. Blockchain Technology can be an appealing alternative as it comes with immutable and decentralized data as well as enhanced security. This study focuses on two methods of securing data management in ERP systems: Organizing the distributed information using The Ralph Kimball data model and optimizing an individual block using Database Sharding. This study does an extensive examination to determine the effectiveness of both suggested strategies, comprising a detailed evaluation that highlights the benefits and limitations of both techniques. This paper intends to patch the security holes in ERP systems to safeguard sensitive data and mitigate risks.

Deciding optimal playing position can sometimes a challenging task for anyone working in sport management industry, particularly football. This study will present a solution by implementing Machine Learning approach to find and help football managers determine and predict where to place individual existing football players/potential players into different positions such as Attacking Midfielder (AM), Defending Midfielder (DMC), All-Around Midfielder (M), Defender (D), Forward Winger (FW), and Goalkeeper (GK) in a specific team formation based on their attributes. To aid in this identification process, it may be beneficial to understand how a player’s playstyle can affect where a player will be positioned in a team formation. The attributes used in facilitating the identification of the player position will be based on Passing Capabilities (AveragePasses), Offensive Capabilities (Possession, etc), Defensive Capabilities (Blocks, Through Balls, Tackles, etc), and Summary (Playtime, Goals, Assists, Passing Percentage, etc). The data that will be analysed upon will be scrapped manually from a popular football site that present football players statistics in a structured and ordered manner using a scrapping tool called Octoparse 8.0. Afterwards, the data that has been processed will be used to create a machine learning predictor modelled using various classification algorithms, which are KNN, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest ,coded using the Python programming language with the help of various machine learning and data science libraries, further enriched with copious graphs and charts which provides insight regarding the task at hand. The result of this study outputted in the form of the model predictor’s evaluation metric proves the Decision Tree algorithm have both the highest accuracy and f1-score of 76% and 75% respectively, while Naïve Bayes sits the lowest at both 69% accuracy and f1-score. The evaluation has prioritized validating and filtering algorithms that have overfitting in copious amounts which are evident in both the KNN and Support Vector Machine algorithms. As a result, the model formed in this study can be used as a tool for prediction in facilitating and aiding football managers, team coaches, and individual football players in recognizing the performance of a player relative to their position, which in turn would help teams in acquiring a specific type of player to fill a systematic frailty in their existing team roster.

Sentiment analysis, the process of determining the emotional tone of a text, is essential for comprehending user opinions and preferences. Unfortunately, the majority of research on sentiment analysis has focused on reviews written in English, leaving a void in the study of reviews written in other languages. This research focuses on the understudied topic of sentiment analysis of Bangla-language product reviews. The objective of this study is to compare the performance of machine learning models for binary and multiclass sentiment classification in the Bangla language in order to gain a deeper understanding of user sentiments regarding e-commerce product reviews. Creating a dataset of approximately one thousand Bangla product reviews from the e-commerce website 'Daraz', we classified sentiments using a variety of machine learning algorithms and natural language processing (NLP) feature extraction techniques such as TF-IDF, Count Vectorizer with N-gram methods. The overall performance of machine learning models for multiclass sentiment classification was lower than binary class sentiment classification. In multiclass sentiment classification, Logistic Regression with bigram count vectorizer achieved the maximum accuracy of 82.64%, while Random Forest with unigram TF-IDF vectorizer achieved the highest accuracy of 94.44%. Our proposed system outperforms previous multiclass sentiment classification techniques by a fine margin.

The IoT technology is used to monitor the child’s safety in the real time. The overall system is controlled by the Raspberry pi 3 module and it is connected to all the IoT devices. The child’s body temperature and pulse rate values are extracted through sensors. Whenever child experiences any abnormal feelings or unusual activities like fear, anxiety, etc. the temperature and pulse rate values reach above threshold and when child speaks with a specific keyword (i.e. “Help Me” or “Save Me”) then the voice recognition system gets activated. The child’s input voice will be converted into text format. When these text formats gets matched with the keywords then immediately pi camera captures the image of an attacker and sends an E-mail alert and at the same time even SMS alert is sent to the parents/guardians. The parents can also watch the live video streaming of the attacker in the YouTube channel from their remote location. This safety system also sends SMS alert message through Twilio in the local language (i.e. Kannada and Hindi) to the registered phone numbers (i.e. surrounding/neighbors of the victim), so that any local citizen can read and understand the message to save the child.