Chronic Kidney Disease (CKD) implies that the human kidneys are harmed and unable to blood filter in the manner which they should. The disease is designated "chronic" in light of the fact that harm to human kidneys happen gradually over a significant time. This harm can make wastes to build up in your body. Many techniques and models have been developed to diagnos the CKD in early-stage. Among all techniques, Machine Learning (ML) techniques play a significant role in the early forecasting of different kinds ailments. ML techniques have been used for achieving analytical results which is one of the instruments utilize in medical analysis and prediction. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Seven ML techniques together with NBTree, J48, Support Vector Machine, Logistic Regression, Multi-layer Perceptron, Naïve Bayes, and Composite Hypercube on Iterated Random Projection (CHIRP) are utilized and assessed using distinctive evaluation measures such as mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), recall, precision, F-measure and accuracy.The experimental outcomes accomplished of MAE are 0.0419 for NB, 0.035 for LR, 0.265 for MLP, 0.0229 for J48, 0.015 for SVM, 0.0158 for NBTree and 0.0025 for CHIRP. Moreover, experimental results using accuracy revealed 95.75% for NB, 96.50% for LR, 97.25% for MLP, 97.75% for J48, 98.25% for SVM, 98.75% for NBTree, and 99.75% for CHIRP. The overall outcomes show that CHIRP performs well in terms of diminishing error rates and improving accuracy.

The discipline of Automatic Text Summarization (ATS), which is expanding quickly, intends to automatically create summaries of enormous amounts of text so that readers can save time and effort. ATS is a rapidly growing field that aims to save readers time and effort by automatically generating summaries of large volumes of text. In recent years, significant advancements have been witnessed in this area, accompanied by challenges that have spurred extensive research. The proliferation of textual data has sparked substantial interest in ATS, which is thoroughly examined in this survey study. Researchers have been refining ATS techniques since the 1950s, primarily categorized as extractive, abstractive, or hybrid approaches. In the extractive approach, key sentences are extracted from the source document(s) and combined to form the summary, while the abstractive approach employs an intermediary representation of the input document(s) to generate a summary that may differ from the original text. Hybrid approaches combine elements of both extractive and abstractive methods. Despite various recommended methodologies, the generated summaries still exhibit noticeable differences compared to those created by humans. This research survey offers an inclusive exploration of ATS, covering its challenges, types, classifications, approaches, applications, methods, implementations, processing and preprocessing techniques, linguistic analysis, datasets, and evaluation measures, catering to the needs of researchers in the field.

Risk prediction is the most sensitive and critical activity in the Software Development Life Cycle (SDLC). It might determine whether the project succeeds or fails. To increase the success probability of a software project, the risk should be predicted at the early stages. This study proposed a novel model based on the requirement risk dataset to predict software requirement risks using Tree-Family -Machine-Learning (TF-ML) approaches. Moreover, the proposed model is compared with the state-of-the-art models to determine the best-suited methodology based on the nature of the dataset. These strategies are assessed and evaluated using a variety of metrics. The findings of this study may be reused as a baseline for future studies and research, allowing the results of any proposed approach, model, or framework to be benchmarked and easily checked.

Automatic text summarization (ATS) has developed as a vital method for compressing massive amounts of textual content into concise and useful summaries, to retrieve more effective and useful information. ATS reduces textual statistics into coherent and shorter versions especially focusing on psychological text summarization to extract insights and emotional states, assisting in better analysis and understanding of psychological contents. In this context, this study proposes a new hybrid model T5-LSTM FusionNet, to enhance textual content summarization in the field of psychology. The motivation derives from the developing extent and accessibility of psychological literature online, which necessitates exceptional techniques for extracting significant findings quickly and reliably. The recommended T5-LSTM FusionNet model combines the benefits of Text-to-Text Transfer Transformer (T5) and Long Short-Term Memory (LSTM). The dataset with 5480 records, accumulated from numerous psychology-associated websites, has been used to verify its performance. T5-LSTM FusionNet's overall performance is evaluated against several latest models along with T5, LSTM, BERT, and DistilBERT. Measures such as accuracy, precision, recall, F1-score, and ROUGE rankings are used to evaluate the model's exceptional summarization. With T5-LSTM FusionNet accomplishing a precision of 0.72, recall of 0.72, F1-score of 0.71, and accuracy of 0.74, the effects show significant improvement over individual models like T5 and LSTM, as well as competitive models like BERT and DistilBERT, in terms of summarization effectiveness and accuracy. Furthermore, T5-LSTM FusionNet plays thoroughly in catching both unigram and bigram overlaps concerning summaries, as proven through a comparison study using ROUGE metrics. This suggests that T5-LSTM FusionNet can retain sequence integrity and relevance in summarizing tasks. This work advances ATS techniques in psychology by presenting a hybrid model that combines sequential and transformer-based learning strategies.

Text emotion detection is an essential task in Natural Language Processing (NLP), with applications in customer support automation, diagnosing mental health, and social media analysis. Yet, precise emotion detection is a difficult problem as human emotional states are subtle, ambiguous, and contextual. Current models typically fail to fully grasp these subtleties. To overcome these limitations, the current study proposes a new hybrid architecture, LSTM Enhanced RoBERTa (LER), that combines the sequential learning ability of Long Short-Term Memory (LSTM) networks with the deep contextual knowledge provided by the RoBERTa transformer model. The LER model proposed here is tested on the popular ISEAR emotion dataset and registers an impressive accuracy of 88%, which surpasses many robust baseline models. The model’s performance is evaluated on standard metrics, such as precision, recall, and F1-score. The results show that the hybrid approach efficiently detects intricate emotional cues, thus improving the state of emotion detection for real-world, context-sensitive applications.

Software Development Organizations develop a huge number of projects on a yearly basis. One of the main issues is that which tool can be selected for an accurate estimation of the cost of software projects. Software cost estimation (SCE) is one of the main objectives of any project. SCE directly introduces almost all management events including resources allocation, project planning, and project bidding. In this study, some important SCE methods have been studied for comparative analysis and this has been concluded that none of the methods are essential inferior or superior to others, as there is no individual approach that is finest for every situation. The selection of the method is depending upon the nature of the project.