Among recommendation systems, collaborative filtering is a widely used method that leverages user preferences and collective actions to provide accurate book recommendations. With so many books available today, it can be harder and harder for readers to find books that suit their interests. As a result, recommender systems have become a vital tool for addressing this problem head-on, attempting to provide users with personalized book recommendations based on their unique interests and preferences. The studies have employed diverse datasets and machine learning technique KNN with Sparse Matrix, and Deep learning algorithm collaborative filtering Neural Network . Preprocessing carried out by Exploratory Data Analysis. These algorithms have demonstrated a significant improvement in recommendation accuracy. The KNN achieved accuracy levels of 81%, 85%, and 93% for different neighbour values 4, 5, 6 while CFNN achieved the accuracy of 95%. The studies have also delved into understanding the impact of various factors on book recommendations, including user preferences and collaborative patterns among readers and it recommends CFNN is suitable method for recommendation system.

Autonomous vehicles (AVs) are revolutionizing Intelligent Transportation Systems by seamlessly exchanging real-time data with other AVs and the network. For humans, controlled transportation has numerous advantages. But safety and security are the main concerns because malicious autonomous vehicles can make consequences. To avoid these consequences nanosensors are integrated with Vision Transformer (ViT) in AV which play a pivotal role in enhancing anomaly detection. To evaluate performance of the proposed framework, various evaluation metrics were employed. The experimental findings are compared with existing models, such as Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Reinforcement Learning (DRL), and Deep Belief Networks (DBN). The result shows that ViT method achieves accuracy, precision, recall of about 92%, 93% and 93%, respectively. Experimental results demonstrate the superiority of ViT and nano sensor integration over traditional methods, showcasing its ability to detect a wide range of attacks with high accuracy and robustness.

China's economic trajectory has garnered significant attention globally, driven by its impressive growth and increased integration into the international trade arena. In response to this, China has strategically implemented a series of trade liberalization policies aimed at fostering economic development, attracting foreign investment, and enhancing global competitiveness. Despite the importance of these policies and their potential impact on the nation's export performance, there is a discernible gap in the literature that necessitates a more sophisticated and forward-looking approach. Traditional forecasting methods applied in economic analyses, while valuable, face challenges in capturing the inherent complexity of economic variables influenced by rapidly changing policies and the dynamic nature of global market forces. Acknowledging this gap, our research is inherently motivated by the urgent need for an accurate and efficient forecasting model capable of navigating the intricate and ever-evolving economic landscape of China. In this study, we introduce an optimal machine learning-based forecasting model to analyze the impact of trade liberalization on China's economy and its export performance. Initially, we extract meaningful features from the provided China's economic dataset, optimizing these features through the modified chicken swarm optimization (MCSO) algorithm. Furthermore, we design the convolutional neural network–bagged decision tree (CNN-BDT) for China's economic forecasting, specifically designed to reduce the false positive rate. Finally, we validate the performance of the proposed CNN-BDT model using sample data of China's exports to the US from 2015 to 2021. The results demonstrate the effectiveness of the proposed CNN-BDT model in terms of performance metrics, including accuracy, precision, recall, and F-measure.

In the present paper, the analysis of growth and instability in production, area and yield of wheat for some wheat growing states of India is carried out by computing compound growth rate (CGR) and Cuddy-Della Valle (CDV) instability index on utilizing secondary time series data of wheat pertaining to the period 2011-2020 in the concerned states. The percentage change in production, area and yield of wheat is examined by considering the base year as 2011. Moreover, the percentage share of production, area and yield of wheat are demonstrated graphically for the year 2020.

Purpose: The purpose of the study is to investigate blue zone lifestyle on Indian diet management system through optimized diet plans. The study explores menu planning with plant-based, animal-, and dairy-based recipes promoting longevity and reduction of chronic diseases in India. Design/Methodology/Approach: The macro- and micronutrient data is collected for the regionally available food items in India. The study proposed linear programming problems to maximize the calories with 66 food items, satisfying the Required Nutrient Intake (RNI) for normal individuals living in rural and urban areas of India. Findings: Three optimization models, such as Linear Programming Problem (LPP), Integer Linear Programming (ILP), and Stigler’s Diet Programming (SDP), were proposed for selecting menus with varying calorie ranges (1900 kcal-3100 kcal). The percentage of nutrients contained in the diet plans was close to Blue Zone food guidelines adoptable to the Indian population. Originality/Value: The revised Stigler Diet Problem (SDP) has well-optimized objective function with the highest accommodation of recipes in optimal menus. This approach is helpful to nutritionists and dieticians for preparing affordable diet plans for distinct income groups. Also, the study provides insights to policymakers working on improving the health conditions of people by adopting the blue zone diet.

This study aims to analyze the effect of the drill method on the mathematics learning outcomes of seventh-grade students at SMP Negeri 5 Jombang. The pre-test results were tested for normality using the Shapiro-Wilk test and for homogeneity using the Bartlett test. The pre-test results showed the data were normally distributed and homogeneous. The t-test indicated that the pre-test mean scores between the control and experimental classes were the same. However, the post-test data for the experimental class were not normally distributed, so homogeneity was tested using the Levene test. The Mann-Whitney test showed a significant difference, proving that the drill method affects students' learning outcomes.

This study aims to determine the effect of resampling RACOG and RACOG-RUS data on Gradient Boosting and Naïve Bayes classification in predicting water quality with unbalanced data. The data used in this study were 720 data from January 2022 to December 2023. It was found that Gradient Boosting performed best when using RACOG-RUS resampling data and feature selection with a number of numIntances of 200. While Naïve Bayes has the best performance when using RACOG-RUS resampling data without feature selection with a number of numIntances of 300. It can be seen that resampling RACOG data does not outperform RACOG-RUS in both classification models because it is known that the data generated in RACOG does not make the dataset more balanced than RACOG-RUS. Hybrid sampling is necessary if RACOG samples are used as the training dataset.

The main key objective of this paper is to address the nonresponse problems by adapting Hansen and Hurwitz’s technique (1964) and Saini et al.’s estimator (2022) to propose a novel estimator of population mean under sub-sampling technique using multiple auxiliary variables. A comparative analysis of the proposed novel estimator's efficacy has been performed through theoretical and numerical studies. The results of this paper confirm that our estimator is more effective than others under the same situation.

The purpose of this paper is to present an analytically easy-to-use procedure for estimating extreme quantiles of continuous random variables using the Peak Over Threshold approach and a statistically sound approach to the problem of threshold selection that needs to be resolved in this context. A web link included in the text points to a ready-to-use implementation of the proposed method in the popular programming language Python.

We propose and develop the four-parameter Harris Extended Fréchet distribution. It is obtained by inserting the two-parameter Frechet distribution as the baseline in the Harris family and may be a useful alternative method to model income distribution and could be applied to other areas. We demonstrate that the new distribution can have decreasing, increasing and upside-down-bathtub hazard functions and that its probability density function is an infinite linear combination of Frechet densities. Some standard mathematical properties of the proposed distribution are derived, such as the quantile function, ordinary and incomplete moments, incomplete moments, Lorenz and Bonferroni curves, Gini index, Renyi and ????-entropies, mean residual life and mean inactivity time, probability weighted moments, stress-strength reliability, and order statistics. We also obtain the maximum likelihood estimators of the model. The potentiality/flexibility of the new distribution is illustrated by means two applications to failure and waiting time data sets