ABSTRACT An important advancement in medical diagnostics is the detection of eye diseases through image analysis, which is facilitated by the development of robust machine‐learning approaches. This research provided an extensive methodology for eye disease classification, introducing the Multi‐Layer DenseNet‐77 architecture for analyzing the images. The process starts with the aggregation of different datasets for various eye conditions, which include healthy eyes, cataracts, diabetic retinopathy, and glaucoma. The data undergoes pre‐processing, including grayscale conversion, resizing, and standardization, to improve image quality and ensure uniformity. Feature extraction is performed to capture important characteristics of the images. First‐order statistics, such as entropy, mean, intensity, and energy, are used for feature extraction. These features serve as inputs to the Multilayer DenseNet‐77 model. To enhance the classification accuracy, the Multilayer DenseNet‐77 model incorporates advanced regularization techniques and optimized training strategies. The architecture of the model facilitates efficient feature reuse through the dense connections, which tackle the challenges associated with gradient flow and overfitting. Through the pooling layers and multiple convolutional layers, the retinal images are processed, and the DenseNet‐77 approach classifies the images into predefined categories that significantly improve the early diagnosis and detection of eye diseases. This method not only contributes to enhancing the outcomes of the patients but also sets the stage for using deep learning in the analysis of medical images, highlighting its potential in transforming healthcare interventions. The experimental result validates that the proposed Multilayer DenseNet‐77 model attained an accuracy of 99.21%, precision of 98.65%, recall of 98.56%, F1‐score of 97.42%, specificity of 98.61%, and dice coefficient of 0.974. The outcomes of the Multilayer DenseNet‐77 model indicate that the model achieved excellent performance in eye disease classification.

With the advancement of Artificial Intelligence in medical and engineering fields, the most unique solutions were deployed in any individual’s life for prolonging their life span against the odds of growing disorders. One such disorder is Lung Cancer which is predominantly found in both women and men causing a disturbed life cycle which could lead to mental stress and even fatal end when unnoticed. In recent times, computer-aided diagnostic (CAD) act as a major automating diagnosing tool by building the self-tailored learning algorithms founded on Classical Machine and Deep Learning model. However, training classical learning frameworks consumes a huge computational resources which leads to complexity and low diagnostic performance. To overcome this problem, Quantum Hippo Optimized Convolution Neural Network (QHO-CNN) has been proposed to homogenize the quantum computations on classical computers which can effectively diagnose lung cancers with the high-speed computations. The proposed learning framework consists of four components namely Data collection & Data pre-processing, Classical Hippo Optimized Convolutional Neural networks, and Quantum based model using parametric quantum circuits (PQC), Evaluation and Analysis. The extensive experimentation carried out using LIDC-IDRI Lung cancer datasets which consists of original 1018 CT Lung Images and various learning capability tests was performed, which is then compared with the other learning framework. Results demonstrate that quantum-based learning framework has produced the accuracy of 0.97, precision of 0.964, recall of 0.963 and F1-score of 0.97 besides showing its strength of success in terms of recognising the image data and quantum training(5.431 HRS) against the other existing quantum models.

Summary Nowadays, research based on mobile ad hoc network's (MANET) performance improvement has drawn a particular concentration from scientists worldwide. The shortest path selection among destination and source nodes determines the network's throughput in MANET. In addition, power management and energy consumption are also the major difficulties that MANET suffers. The conventional routing protocols are intended for static bonding, but that protocol does not satisfy the stability in ad hoc networks by means of regularly changing connectivity. This shows the routing problem in MANETs. Therefore, to overcome the routing issues and maintain energy efficiently, the Type‐2 fuzzy‐based Starling Murmuration Optimizer (SMO)‐War Strategy Optimization (WSO) (T2FSMO‐WSO) routing protocol is proposed in this paper. MANET takes into account four input parameters: route length, active link ratio to global traffic, network lifetime, and control packet ratio. The SMO algorithm is employed to choose the relay node with low energy consumption. The trapezoidal membership functions are used to describe the fuzzy sets for these parameters. The content‐based routing among source and destination nodes is determined using the WSO algorithm. The simulation outcomes reveal that the proposed method discovers highly stable paths with minimum distance and minimized the consumption of energy.

Abstract In terms of training students for work in diverse firms, traditional and out‐of‐date teaching techniques cannot compete with digital teaching methods. To overcome this problem, the teaching approach and content must be changed. An Educational Assistant for Software Testing (EAST) framework is developed in this work to train students to improve their skills in software testing via Computer Assisted Instruction (CAI) built using Natural Language Processing (NLP), Machine learning, and information retrieval techniques. In this paper, a Group Search Optimized two‐stage hybrid Support Vector Machine‐K‐Nearest Neighbor (SVM‐KNN) classifier is used to develop a novel approach for analyzing the parameters that introduce bugs in bug reports. To decrease the data sparsity problem, the group search optimization (GSO) algorithm is used to improve the parameter selection process of the two‐stage hybrid classifier by generating optimal values for parameters such as k, c, and gamma. Two bug report datasets were used to test the model. The database for our application is built by collecting bug reports from a wide open‐source community as well as several mobile application development companies. Based on the extensive experiments conducted via different performance metrics, we can conclude that the EAST framework can improve outdated teaching methodologies.