KLE Society's Dr. M. S. Sheshgiri College of Engineering and Technology, formerly KLE Society's College of Engineering and Technology (KLESCET), was established in the year 1979 at Belgaum, Karnataka, India. The campus has an area of 17 Acres. It is self-financing Institute recognized by All India Council for Technical Education and is affiliated to Visvesvaraya Technological University.
Accurate real-time coral reef bleaching classification is challenging due to the high computational cost and limited interpretability of conventional convolutional neural networks (CNNs), which restrict deployment on resource-constrained edge devices. To address this, we propose Fibonacci-Net (F-Net), a lightweight and interpretable CNN that integrates Fibonacci-based filter scaling, a patch-based hybrid area-attention mechanism to enhance fine-grained coral features, and a particle swarm optimization-Adam hybrid optimizer for stable learning on small, imbalanced datasets. Evaluated on 7384 coral images, F-Net achieves 97.6% accuracy, better than some well-studied CNN models in the literature. The novel gradient-weighted class activation mapping and filter discriminability analyses further enhance interpretability, demonstrating F-Net's effectiveness and deployment readiness for large-scale autonomous coral reef monitoring.
Diagnosis of prostate cancer is an area of medical research of critical importance, in which advancements in imaging technologies have much improved detection as well as treatment outcomes. Although substantial progress has been made concerning the application of machine learning (ML) and deep learning (DL) techniques, few systematic reviews have examined these techniques in the context of multiparametric MRI (mpMRI) and diffusion-weighted synthetic imaging (DWSI). Existing studies often focus on individual methods or imaging modalities, leaving a gap in understanding how these techniques are integrated and optimized for diagnostic precision. This motivated this review paper to comprehensively review and summarize the new automated methods for prostate cancer diagnosis, particularly through the use of mpMRI and DWSI imaging. It explores various imaging modalities and their integration with DL and ML techniques to improve diagnostic accuracy. The review assesses the effectiveness of these advanced imaging approaches in Gleason score (GS) estimation and highlights the challenges associated with each modality. The review systematically compares performance evaluated by specific feature values such as specificity, F-measure, precision, and accuracy of several ML and DL algorithms for prostate cancer diagnosis. Alongside this, the review brings to attention the current limitations of the approaches and points to future research directions with an emphasis on the innovative requirement for finding better generalization methods to mitigate diagnosis problems in prostate cancer management.
This paper introduces an innovative hybrid method for the classification of Gleason Scores (GS) in prostate cancer using Whole Slide Images. The researchers combine Deep Learning and Machine Learning (ML) techniques to automate the precise classification of GS. They employ a specially designed Variational Autoencoder for feature extraction, utilizing a pre-trained VGG16 Convolutional Neural Network to build the encoder. Principal Component Analysis is then used to reduce the dimensionality of the feature vector to 50 significant features for further Gleason Grade classification. The study uses the SICAPv2 database and evaluates feature importance with Shapley Additive explanations (SHAP). Comparative analysis of five ML techniques and two custom-designed Deep Neural Network (DNN) architectures shows that the Support Vector Machine algorithm with hyperparameter tuning and the custom-designed five-layer DNN architecture achieved accuracies of 84
Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. By using the Programming interface key, Twitter data for this article was collected from Twitter. Similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.
Continuous reactive distillation is one of the optimized methods for synthesis of C1-C6 esters industrially. The products of the reaction are difficult to be obtained in their pure states due to the presence of azeotropes in the system. The theme of the current work is to highlight the thermodynamic nature of the each C1-C6 alcohol/ester/water system independently and investigate the possible alternatives implemented for pure separation of ester and water. This work comprehensively reports reactive distillation study for continuous synthesis of C1-C6 esters and separation strategies implemented for alcohol/ester/water system in a compiled form. Presence of the azeotropes within the system components helps us in defining the feasible separation schemes to be applied in continuous study. Current work reports a systematic thermodynamic analysis of C1-C6 alcohol/esters/water mixture with help of residue curve map to investigate the complexity of the mixture and alternate configurations/ schemes implemented in continuous reactive distillation technology for enhanced conversion and purity of product streams.
