Effort estimations are a crucial aspect of software development. The tasks should be completed before the start of any software project. Accurate estimations increase the chances of project success, and inaccurate information can lead to severe issues. This study systematically reviewed the literature on effort-estimating models from 2015-2024, identifying 69 relevant studies from various publications to compile information on various software work estimation models. This review aims to analyze the models proposed in the literature and their classification, the metrics used for accuracy measurement, the leading model that has been chiefly applied for effort estimation, and the benchmark datasets available. The study utilized 542 relevant articles on software development, cost, effort, prediction, estimation, and modelling techniques in the search strategy. After 194 selections, the authors chose 69 articles to understand ML applications in SEE comprehensively. The researchers used a scoring system to assess each study’s responses (from 0 to 5 points) to their research questions. This helped them identify credible studies with higher scores for a comprehensive review aligned with its objectives. The data extraction process identified 91% (63) of 69 studies as either highly or somewhat relevant, demonstrating a successful search strategy for analysis. The literature review on SEE indicates a growing preference for ML-based models in 59% of selected studies. 17% of the studies chosen favor hybrid models to overcome software development challenges. We qualitatively analyzed all the literature on software effort estimation using expert judgment, formal estimation techniques, ML-based techniques, and hybrid techniques. We discovered that researchers have frequently used ML-based models to estimate software effort and are currently in the lead. This study also explores the application of feature importance and selection in machine learning models for Software Effort Estimation (SEE) using popular algorithms like support Vector Machine (SVM), AdaBoost (AB), Gradient Boost (GB), and Random Forest (RF) with six benchmark datasets like CHINA, COCOMO-NASA2, COCOMO, COCOMO81, DESHARNAIS, and KITCHENHAM. We analyze the dataset descriptions and feature importance of the dataset analysis using ML models for choosing crucial play attributes in SEE.

Touch screen interaction system is highly demanding for new innovations such as visual sensing, virtual key board system on the screen, three dimensional gesture communications methods, and RFID sensing etc. In spite of the existence of these types of interaction methods, visually impaired people struggle to have easy access to touch screens. The main goal of the research is to overcome the navigation problems that blind people face while interacting with touch screens. In this examination we centered to build up a Braille sketch; a motion put together information technique with respect to contact screens of advanced mobile phones for outwardly debilitated individuals. Utilizing Braille codes to perform motions on contact screen makes the outwardly tested people agreeable in light of the fact that “Braille is the reason for correspondence”. The streamlining procedure is the demonstration of amplifying or limiting a genuine function by systematic choosing input parameters from an accessible pool of parameters and to figure the estimation of function. Here, we accept variables as hand finger motion facilitate, for example, the coordinate values on x and y axises, swipe limit speed, swipe least separation, pixel rate and speed of X and Y. To build the execution of the system structure, we improve by differing shrouded layer and neuron using Crow Search Algorithm (CSO). The ANN with CSA achieves the Optimal Hidden Layer and Neuron (OHLN) to anticipate the right motion yields. These strategies present an answer that will consequently perceive the hand signals so impeded individual can without much of a stretch speak with ordinary individuals. The proposed model will give high precision with ideal execution measurements contrasted with other existing created demonstrate.

The early diagnosis of diabetes helps in avoiding the major risks associated with the disorder. The proposed research involves the design of a machine learning pipeline which generates the most representative feature subset of minimal size that predicts the onset of Diabetes with highest accuracy. It employs a novel diabetes dataset which is gender-neutral and representative enough unlike the well-known PID dataset. The machine learning pipelines involve multiple feature engineering pipelines to generate a reduced feature subset which is fed into multiple heterogeneous classifiers. The feature engineering involves feature selection as well as feature extraction. The former uses the ANOVA filter and Crow Search Optimization algorithm. The latter employs the Singular Value Decomposition. The classification is performed on the preprocessed dataset using a wide range of heterogeneous classifiers like Naive Bayes’, Logistic Regression, K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forest, AdaBoost, and GradientBoost as base learners followed by their stacking ensemble. The performance evaluation of each machine learning pipeline is done through Repeated Stratified K-fold Cross Validation using the metrics of accuracy, precision, recall, F1 Score and area under Receiver Operating Characteristic curve. For each pipeline, the number of features in the preprocessed dataset varies and the highest accuracy of 98.4% is achieved with Crow Search algorithm through a stacking ensemble of multiple heterogeneous classifiers. A comparative analysis with a recent related work on the same dataset shows that the proposed feature engineering pipelines with the same set of classifiers outperform with improved accuracy using a feature set of reduced size.

Hitherto, Rice (Oryza Sativa) has been one of the most demanding food crops in the world, cultivated in larger quantities, but loss in both quality and quantity of yield due to abiotic and biotic stresses has become a major concern. During cultivation, the crops are most prone to biotic stresses such as bacterial, viral, fungal diseases and pests. These stresses can drastically damage the crop. Lately and erroneously recognized crop diseases can increase fertilizers costs and major yield loss which results in high financial loss and adverse impact on nation’s economy. The proven methods of molecular biology can provide accurate detection of pathogenic factors, but these methods are not accessible to the majority of the farmers, needs high costs or resources, and require domain knowledge to implement. Expert’s field inspection report provides precise crop diagnosis but continuous field inspection over the remotely placed agriculture fields is not feasible. Therefore, cost effective approach for early detection of diseases can help farmers to take necessary steps in time to boost up the crop production. Precision agriculture makes use of decision support systems built using Machine Learning (ML) or Deep Learning (DL) approaches to cut down heavy costs. Timely crop diagnosis process can be automated with the involvement of Computer Vision, Image Processing and Deep Learning (DL) based methods for more precise prediction in less cost and time. Latest research shows that more accurate image classification can be implemented using Deep Learning based Convolutional Neural Network (CNN) model. In this paper, we have proposed an automated Rice Disease Diagnosis System (RDDS) for timely, more accurate and detailed crop disease diagnosis, which consisting of two modules, they are Leaf Disease Identification (LDI) module for disease detection and Infection Intensity Estimation (IIE) module for disease severity analysis. The LDI module is based on the proposed novel RDD_CNN model that classified the eight most harmful and commonly occurring diseases, it has obtained the best test accuracy of 98.47% when compared to its first three versions. And the IIE module is designed for estimating identified disease’s intensity in terms of extent and stage of infection providing detailed and overall diagnosis report specially designed for Brown Spot disease.

Agriculture 5.0 is a principal economic activity in the world with major workforce dependent crops cultivation. An automated system for crops field insect pest identification can help decrease labour, while also improving the speed and precision in compared to manual methods. Less computation and memory systems are getting utilized for remote deployments of classification systems. In this paper, efficient pretrained multiple deep learning models based visual feature extraction and Linear Discriminant Analysis (LDA) based feature selection to provide high efficacy with light resource requirements. This proposed approach also able to handle large number of classes. To achieve this, diverse pest datasets were combined including 9 and 12 classes respectively and the utilized combined dataset contains total 19 classes. Methodology of proposed system included the selection of multiple pretrained models including DenseNet201, EfficientNetB3 and InceptionResNetV2 based on less memory and less parametric requirements. The second last layer of mentioned models have been utilized for selection of features as DenseNet201, EfficientNetB3 and InceptionResNetV2 resulted 1920, 1536 and 4608 accordingly. The extracted features combined and selected using LDA according to the number of classes. At end a basic light dense neural network have been deployed for classification. This makes a low resource and high efficacy pest classification model for higher number of classes. The results obtained by proposed technique are 99.99% Accuracy, 100% validation, 99.99% Recall and negligible Loss. Further the proposed system has been analysed and compared with benchmark approaches including transfer learning and single model feature extraction and selection approach. The main advantage of our proposed hybrid feature selection is that it makes the classification process lighter because it involves selecting relevant features from the existing dataset without training additional models, whereas transfer learning typically involves retraining or fine-tuning pre-existing models, which can be more computationally intensive. Overall the proposed system and approach resulting higher results with lighter process resources that fitted better in the development of domain of precision agriculture.