Retrospection of the Optimization Model for Designing the Power Train of a Formula Student Race Car

Smart Farming System Based on Intelligent Internet of Things and Predictive Analytics

The Internet of Things (IoT) makes it conceivable to communicate among distinctive things. The use of IoT in the farming industry is critical for increasing utility. Smart agricultural practices may boost crop yield while also creating more output with the same amount of input. The majority of farmers, however, are still unaware of the most recent technologies and procedures. In this study, a revolutionary wireless mobile robot based on the Internet of Things (IoT) is created and installed to perform a variety of outdoor tasks. The benefits of this work include more accurate and efficient data, as well as a reduction in manpower. This research has applications in agriculture, arrival, and water division. Keen agrarian frameworks have been built up in different parts of the world utilising the Internet of Things (IoT) and remote sensor systems. One of the branches that springs to intellect in this respect is exactness cultivating. Numerous analysts have made checking and robotization frameworks for different cultivating capacities. Information collection and transmission between IoT gadgets set in ranches will be basic utilising WSN. The Kalman Filter (KF) is used with expectation investigation within the proposed method to get high-quality information free of commotion and exchange it with cluster-based WSNs. The quality of information utilised for examination is progressed as a result of this strategy, and information transport overhead within the wireless sensor network application is decreased. A decision tree is used for forecast analytics decision making for trim surrender expectation, trim classification, soil classification, climate expectation, and trim malady expectation. IoT components integrated with IoT cloud are coordinates in proposed framework to supply keen arrangement for edit development observing to clients.

User Classification and Stock Market-Based Recommendation Engine Based on Machine Learning and Twitter Analysis

The stock market prices of the company vary in a daily fashion. The social media pattern usage of the company can be determined to find the sentiment score values. The dependency factor between the social media tweet platform and the performance of an organization can have how much effect on the stock prices is determined. The historical data from the Yahoo Finance APIs are taken for the unique company ID and then the probability of stock being good or bad is determined. Also, the tweets related to the company are scanned and analyzed to find the positive and negative scores. The concentration value connected to growth, the intensity of capital expenditure, and the volume of promotion were among the factors utilized in the stock’s modeling. This paper also takes the yearly finances of the end-user based on LIC payments, medical insurance payments, and average rent and then performs a classification of the user. Based on the user classification, companies are recommended to the end-user based on descending order of stock value. The average volume, average price, average market index, average daily turnover, and sentiment discrepancy index are based on the tweets of a company and the predicted value of its performance. For the classification of the user, we make use of the support vector machine algorithm. For the sentiment analysis of the tweets, the naïve Bayes algorithm is made use of, and then stock classification is done based on mathematical modeling, which includes the sentiment analysis index.

Elucidation of Nature of Gene Action and Estimation of Combining Ability Effects for Fruit Yield Improvement and Yield Attributing Traits in Brinjal Landraces

Genetic progress in quantitative traits can be improved by understanding how genes interact and estimating the consequences of combining abilities. As a result, a randomized block design with three replications was used to conduct forty crossings using a line X tester mating design with ten lines and four testers. All of the qualities were shown to be highly variable based on the ANOVA (analysis of variance) results among lines, testers, and hybrids. An estimated predictability ratio showed a high prevalence of nonadditive gene action, which was further confirmed by the lower narrow-sense heritability values for all traits. Most of the characters had high general combining ability and specific combining ability estimates, showing the relevance of both additive and nonadditive gene effects, respectively. For all of these features, however, the specific combining ability variations were greater than the general combining ability variances. Since heterosis breeding can lead to better hybrids, it may be a good idea to do so. For most yield-related parameters, such as fruit diameter, fruit per plant, marketable fruit per plant, yield per plant, marketable yield per plant, and total yield, RKML-26 and RKML-34 were the best general combiners among all lines. So, these lines might be employed as parents in hybridization programme in future to get suitable recombinants for higher fruit yield. However, the best cross combinations for commercial hybrid exploitation were RKML-26 X Pusa purple cluster (PPC) and RKML-2 X Swarna Shyamli. These crosses exhibiting higher per se performance and desirable specific combining ability effects together with either both or at least one parent as a competent combiner would be rewarding for heterosis breeding. Combining traditional breeding methods with biotechnological approaches, according to a new study, is critical for the transfer of favorable genes (traits) into farmed plants.

Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques

Agricultural producers and enterprises face a dizzying array of decisions every day, and the many factors that influence them are incredibly complex. Agricultural planning relies heavily on accurately calculating the yields of the various crops that will be used. If you want realistic and successful solutions, data mining is an essential component. Researchers in this study are looking for ways to evaluate agricultural data and extract valuable information from the results in order to increase agricultural output. Use of the CART and random forest algorithms is a data mining technique that may be used to various datasets. It is possible to recognise the effects of various climatic and other factors on agricultural output using the MATLAB software and data mining methods, and a potential strategy is highlighted.

Artificial Neural Network-Based Identification of Associations between UCP2 and UCP3 Gene Polymorphisms and Meat Quantity Traits

In identifying mutations occurring in distinct cow breeds, genetic elements must be taken into consideration. More recently, these hereditary features have gained attention throughout the world. As in many underdeveloped nations, to bridge the deficit in molecular genetics, multiple solutions are required. The inner membrane anion carrier superfamily contains the uncoupling proteins (UCPs), vital to energy regulation. Research on heredity has shown that variations in the UCP2 and UCP3 genes are connected to obesity and metabolic syndrome. This research aimed to investigate if any mutation in the UCP 2 and UCP 3 genes are related to many characteristics in Pakistan’s three indigenous cattle breeds using artificial neural network (ANN). For better analysis, the output of the ANN model is loaded into the Primer Premier 3 software. Using polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) and sequencing, the results of this study indicated 07 variations in the exon 4 region of the UCP2 gene and 03 variants in the exon 3 area of the UCP3 gene among 215 indigenous cow breeds. The association study revealed that the g.C35G mutation in the UCP3 gene is strongly related to meat quantity characteristics such as carcass weight and drip percentage (P0.05) but not with body height or hip width ( $P>0.05$ ). Sequence analysis showed five distinct diplotypes: AA, BC, AC, CC, and CD. Cattle with the novel heterozygous diplotype BC perform better in carcass trait and drip percentage than animals with other genotypes. The study’s findings suggest that the UCP3 gene may be utilized for marker-assisted selection (MAS) and breed mixing in Pakistan cattle breeds to aid in the country’s economic growth.

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning.

Critical Retrospection of Performance of Emerging Mobile Technologies in Health Data Management

The recent advancement in mobile technologies has led to opening a new paradigm in the field of medical healthcare systems. The development of WBAN sensors, wearable devices, and 5G/6G wireless technology has made real-time monitoring and telecare of the patient feasible. The complex framework to secure sensitive data of the patient and healthcare professionals is critical. The fast computation of health data generated is crucial for disease prediction and trauma-related services; the security of data and financial transactions is also a major concern. Various models, algorithms, and frameworks have been developed to tame critical issues related to healthcare services. The efficiency of these frameworks and models depends on energy and time consumption. Thus, the review of recent emerging technologies in respect of energy and time consumption is required. This paper reviews the developments in recent mobile technologies, their application, and the comparative analysis of their performance parameters to explicitly understand the utility, capacity, and limitations. This will help to understand the shortcomings of the recent technologies for the development of better frameworks with higher performance capabilities as well as higher quality of services.

Optimal Matching Metaheuristic Algorithm for Potential Areas of Agricultural Economic Resources Development Based on Spatial Relationship

The agriculture sector is the backbone of the economies of many Asian countries such as India, China, and Bangladesh. The agriculture sector can contribute a major share to the GDP of such countries where the main occupation of the citizens is agriculture or the dependency of the citizens is mainly on the agricultural productivity. It is important to study the potential areas of agricultural economic resource development. The existing methods are not efficient enough to map the potential areas of agricultural productivity with economic resource development, and hence, it has motivated us to study the aspects which impact the economic resource development based on agricultural productivity. There are numerous factors such as low productivity, high irrigation amount, high labor charges, low proportion of planning optimization, and low crop yield that should be considered to study the correlation between economic development and agricultural productivity. Firstly, the spatial relationship of potential areas of agricultural economic resources development is analyzed in this paper. Secondly, the multiobjective linear programming model is proposed. Based on this multiobjective model, the optimal matching model for potential areas of agricultural economic resource development is constructed, and the improved genetic algorithm is used to solve the model to realize the optimal matching of potential areas of agricultural productivity and economic resource development. The experimental results show that the proposed method has high economic benefit, low irrigation amount, and high proportion of planning optimization with high crop yield.

Analysis Method of Agricultural Total Factor Productivity Based on Stochastic Block Model (SBM) and Machine Learning

When analyzing agriculture’s total factor productivity, traditional measurement approaches do not take into account the inefficiency value. The production functions are assumed to be analyzed on basis of the random boundaries, which makes the analysis results inaccurate and unreliable. As a result, this paper proposes an analytical approach for agricultural total factor productivity based on the stochastic block model (SBM), which combines the benefits of statistics and machine learning. It uses the SBM direction distance function and the Luenberger productivity index to measure the agricultural efficiency, total factor productivity, and their components. The research study considers the data from 31 provinces from 2006 to 2018 years. First, one output indicator and six input indicators are established. The time-varying variations of the national agricultural inefficiency value and its source decomposition under variable scale returns are then determined using the SBM-based algorithm of agricultural total factor productivity and the obtained sample data. The changes of the agricultural total factor productivity and its components are comprehensively analyzed. Following an examination of the elements impacting agricultural efficiency and productivity, the socioeconomic development of the agricultural total factor productivity is examined in order to achieve efficient growth.

Retrospection of Nonlinear Adaptive Algorithm-Based Intelligent Plane Image Interaction System

This paper introduces the application and classification of an adaptive filtering algorithm in the image enhancement algorithm. And the filtering noise reduction impact is compared using MATLAB software for programming, image processing, LMS algorithm, RLS algorithm, histogram equalisation algorithm, and Wiener filtering method filtering noise reduction effect. To optimize the intelligent graphic image interaction system, the proposed nonlinear adaptive algorithm of intelligent graphic image interaction system research is based on the digital filter and adaptive filtering algorithm for simulation experiment. The experimental results of several noise index data filtering algorithms show that the fuzzy coefficient k of LMS index is 0.86, RLS index is 0.91, the histogram equalization index is 0.53, and the Wiener filtering index is 0.62. LMS index of quality index Q is 0.90, RLS index is 0.95, histogram equalization index is 0.58, Wiener filtering index is 0.65. According to the above results, comparing LMS with the RLS method and according to SNR, k, and Q values in the simulation results in the process of processing, it is found that the convergence speed of the RLS algorithm is obviously better than that of the LMS algorithm, and the stability is also good. Additionally, the differential imaging data can provide a strong reference for the clinical diagnosis and qualitative differentiation of TBP and CP, and MSCT is worthy of extensive application in the clinical diagnosis of peritonitis. The processing effect of the image with high similarity to the original image is greatly improved compared with the histogram equalization and Wiener filtering methods used in the simulation.

Deep Learning in Healthcare System for Quality of Service

Deep learning (DL) and machine learning (ML) have a pivotal role in logistic supply chain management and smart manufacturing with proven records. The ability to handle large complex data with minimal human intervention made DL and ML a success in the healthcare systems. In the present healthcare system, the implementation of ML and DL is extensive to achieve a higher quality of service and quality of health to patients, doctors, and healthcare professionals. ML and DL were found to be effective in disease diagnosis, acute disease detection, image analysis, drug discovery, drug delivery, and smart health monitoring. This work presents a state-of-the-art review on the recent advancements in ML and DL and their implementation in the healthcare systems for achieving multi-objective goals. A total of 10 papers have been thoroughly reviewed that presented novel works of ML and DL integration in the healthcare system for achieving various targets. This will help to create reference data that can be useful for future implementation of ML and DL in other sectors of healthcare system.

Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease

Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual's health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient's stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient's kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.