Search

In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting. [ABSTRACT FROM AUTHOR]

“Multiple Disease Prediction” is a system based on predictive modeling that will predict the user's disease based on the given symptoms. We have considered four diseases for now, that is Heart disease, Liver disease, Chronic Kidney Disease, and Diabetes. There are existing systems that can predict one disease at a time and perhaps with lower accuracy, which puts a patient’s health in danger. In this project, we aim to design a multiple disease prediction system by applying principles of machine learning, where our application incorporates two ways of predicting diseases for two different categories of users. For single users, the user must enter various parameters of the disease, and the system would display the output whether she/he has the disease or not. The project makes it easier for a multitude of users, such as a hospital management system, to anticipate diseases from the results of laboratory blood test reports. Blood analysis is a crucial disease indicator since it comprises several factors that are symptoms of diseases. This project not only emphasizes prediction precision but also opens the possibility of easily introducing new diseases. [ABSTRACT FROM AUTHOR]

Dengue Virus (DENV) infection is one of the rapidly spreading mosquito-borne viral infections in humans. Every year, around 50 million people get affected by DENV infection, resulting in 20,000 deaths. Despite the recent experiments focusing on dengue infection to understand its functionality in the human body, several functionally important DENV-human protein-protein interactions (PPIs) have remained unrecognized. This article presents a model for predicting new DENV-human PPIs by combining different sequence-based features of human and dengue proteins like the amino acid composition, dipeptide composition, conjoint triad, pseudo amino acid composition, and pairwise sequence similarity between dengue and human proteins. A Learning vector quantization (LVQ)-based Compact Genetic Algorithm (CGA) model is proposed for feature subset selection. CGA is a probabilistic technique that simulates the behavior of a Genetic Algorithm (GA) with lesser memory and time requirements. Prediction of DENV-human PPIs is performed by the weighted Random Forest (RF) technique as it is found to perform better than other classifiers. We have predicted 1013 PPIs between 335 human proteins and 10 dengue proteins. All predicted interactions are validated by literature filtering, GO-based assessment, and KEGG Pathway enrichment analysis. This study will encourage the identification of potential targets for more effective anti-dengue drug discovery. [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) is one of the deadly arboviruses, which is primarily transmitted by Aedes aegypti, and causes dengue infection to the humans. According to WHO, every year around 390 million humans are affected by DENV, of which around 50 million deaths are reported. Knowledge of the various diseases caused by the DENV would greatly encourage to understand the infection mechanism and help to design new antiviral drug discovery. We propose a quasi-clique and quasi-biclique algorithm to classify infection gateway proteins of the human body and possible pathways of DENV leading to various diseases. For this, we have examined three networks, dengue-human protein–protein interaction network, human protein interaction network, and human proteins-disease association network. The prediction result states that DENV may lead to various diseases in the human body, including cancer, asthma, ulcerative colitis, multiple sclerosis, premature birth, and so on. Some of the results have recently been validated experimentally. This study may endow with potential targets for more effective anti-dengue remedial contribution. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) have been widely used to understand different biological processes and cellular functions associated with several diseases like cancer. Although some cancer‐related protein interaction databases are available, lack of experimental data and conflicting PPI data among different available databases have slowed down the cancer research. Therefore, in this study, the authors have focused on various proteins that are directly related to different types of cancer disease. They have prepared a PPI database between cancer‐associated proteins with the rest of the human proteins. They have also incorporated the annotation type and direction of each interaction. Subsequently, a biclustering‐based association rule mining algorithm is applied to predict new interactions with type and direction. This study shows the prediction power of association rule mining algorithm over the traditional classifier model without choosing a negative data set. The time complexity of the biclustering‐based association rule mining is also analysed and compared to traditional association rule mining. The authors are able to discover 38 new PPIs which are not present in the cancer database. The biological relevance of these newly predicted interactions is analysed by published literature. Recognition of such interactions may accelerate a way of developing new drugs to prevent different cancer‐related diseases. [ABSTRACT FROM AUTHOR]

Security is one of the major concerns in generic computing system which is used in our day to day life to deal with various aspects like education, banking, communication, entertainment etc. Security is obtained to prevent threats that usually affect the end users of other areas as well (like grid computing, cloud computing etc.). Malicious code deployment is the main cause of threat. This paper mainly proposes a novel malicious code detection technique (Rule based probabilistic malware detection (RBPMD)) which provides security in the traditional computing system. In this paper, RBPMD technique follows a rule based probabilistic technique to detect the malicious codes among several codes and warn the other non-infected guest machines about it. A prototype of RBPMD is designed and implemented in this paper. It creates less overhead as well as occupies less storage space compare to other well-known anti-viruses. Its implementation cost is also low compare to others. At last, a comparison analysis of this proposed technique is given with other popular malware detection techniques. [ABSTRACT FROM AUTHOR]

We talk much about information literacy, but we are not concerned about the present literacy condition and economical condition in India. Do we have the enough resource, manpower, matured thinking toward accepting a new technology? Whereas infanticide, dowry death, honour killing are still prevalent in our society. How information literacy is helpful to eradicate social evils from our society, encouraging mass power is our motto. Research is needed in this field. In this article I have try to find out the information literacy which is able to enrich the human resource in manifold ways. Information literacy is required for Indian women. In India, every passing five year plan realize the need of ICT and give much importance on it and felt that Indian women also awaited for it. [ABSTRACT FROM AUTHOR]

"Clustering" the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets usingtypical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms. [ABSTRACT FROM AUTHOR]