Machine Learning (ML) such as Artificial Neural Network (ANN), Deep learning, Recurrent Neural Networks (RNN), Alex Net and ResNet can be consider as a broad research direction in the identification and classification of critical diseases. CNN and its particular variant usually named as U-Net Segmentation has made a revolutionary advancement in the classification of medical diseases, specifically retinal diseases. However, because of the feature extraction complexity, U-Net has a significant flaw of high memory and CPU consumption, while moving the whole feature map to the related decoder. Furthermore, it can be concatenated to the unsampled decoder feature map avoids the reusing pooling indices. In this research work, a convolutional neural network (CNN) model is proposed for multi-class classification problem with the efficient use of memory consumption. The proposed model has been evaluated on standard benchmark dataset of Eye Net having 32 classes of retinal diseases. From experimental evaluation it has been concluded that the proposed model performs better in terms of memory management and accuracy. The overall comparison has been performed on the bases of precision, recall, accuracy with different number of epochs and time consumption by each step. Proposed technique achieved an accuracy of 95% on Eye-net dataset.

Electricity theft presents a substantial threat to distributed power networks, leading to non-technical losses (NTLs) that can significantly disrupt grid functionality. As power grids supply centralized electricity to connected consumers, any unauthorized consumption can harm the grids and jeopardize overall power supply quality. Detecting such fraudulent behavior becomes challenging when dealing with extensive data volumes. Smart grids provide a solution by enabling two-way electricity flow, thereby facilitating the detection, analysis, and implementation of new measures to address data flow issues. The key objective is to provide a deep learning-based amalgamated model to detect electricity theft and secure the smart grid. This research introduces an innovative approach to overcome the limitations of current electricity theft detection systems, which predominantly rely on analyzing one-dimensional (1-D) electric data. These approaches often exhibit insufficient accuracy when identifying instances of theft. To address this challenge, the article proposes an ensemble model known as the RNN-BiLSTM-CRF model. This model amalgamates the strengths of recurrent neural network (RNN) and bidirectional long short-term memory (BiLSTM) architectures. Notably, the proposed model harnesses both one-dimensional (1-D) and two-dimensional (2-D) electricity consumption data, thereby enhancing the effectiveness of the theft detection process. The experimental results showcase an impressive accuracy rate of 93.05% in detecting electricity theft, surpassing the performance of existing models in this domain.

Accurate crop yield forecasting is vital for ensuring food security and making informed decisions. With the increasing population and global warming, addressing food security has become a priority, so accurate yield forecasting is very important. Artificial Intelligence (AI) has increased the yield accuracy significantly. The existing Machine Learning (ML) methods are using statistical measures as regression, correlation and chi square test for predicting crop yield, all such model’s leads to low accuracy when the number of factors (variables) such as the weather and soil conditions, the wind, fertilizer quantity, and the seed quality and climate are increased. The proposed methodology consists of different stages, like Data Collection, Preprocessing, Feature Extraction with Support Vector Machine (SVM), correlation with Normalized Google Distance (NGD), feature ranking with rising star. This study combines Bidirectional Gated Recurrent Unit (Bi-GRU) and Time Series CNN to predict crop yield and then recommendation for further improvement. The proposed model showed very good results in all datasets and showed significant improvement compared to baseline models. The ECP-IEM achieved an accuracy 96.34%, precision 94.56% and recall 95.23% on different datasets. Moreover, the proposed model was also evaluated based on MAE, MSE, and RMSE, which produced values of 0.191, 0.0674, and 0.238, respectively. This will help in improving production of crops by giving an early look about the yield of crops which will than help the farmer in improving the crops yield.

As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

Requirement elicitation plays an important role during the software development life cycle. The selection of an improper requirement elicitation method will affect the quality of developed software. Agile methodologies are popular in the industry and follow an incremental approach to developing software. Agile methodologies value customer needs, interaction among teams, interaction with customers, and change management. Researchers proposed methods for requirement elicitation in agile software development. This research aims to investigate the issues faced during requirement elicitation in agile software development. We will identify the method that motivates the requirements elicitation in agile software development to meet our objective. After identifying the literature, a systematic literature review will be performed. An introductory overview, publications trends and values, strengths, and limitations will be highlighted. Based on the identified limitations, we will propose a new requirement elicitation method useful in agile software development. To evaluate the results, two teams of equal expertise were given the same project to develop. One of the teams developed using the proposed framework, and the other one did without using the proposed framework. Then, both of them were given the survey they filled out and gave their input on the requirement elicitation parameters, and the results were compared and validated using a t-test and reliability analysis.