Breast Cancer Detection and Analytics Using Hybrid CNN and Extreme Learning Machine.

Early detection of breast cancer is essential for increasing survival rates, as it is one of the primary causes of death for women globally. Mammograms are extensively used by physicians for diagnosis, but selecting appropriate algorithms for image enhancement, segmentation, feature extraction, and classification remains a significant research challenge. This paper presents a computer-aided diagnosis (CAD)-based hybrid model combining convolutional neural networks (CNN) with a pruned ensembled extreme learning machine (HCPELM) to enhance breast cancer detection, segmentation, feature extraction, and classification. The model employs the rectified linear unit (ReLU) activation function to enhance data analytics after removing artifacts and pectoral muscles, and the HCPELM hybridized with the CNN model improves feature extraction. The hybrid elements are convolutional and fully connected layers. Convolutional layers extract spatial features like edges, textures, and more complex features in deeper layers. The fully connected layers take these features and combine them in a non-linear manner to perform the final classification. ELM performs classification and recognition tasks, aiming for state-of-the-art performance. This hybrid classifier is used for transfer learning by freezing certain layers and modifying the architecture to reduce parameters, easing cancer detection. The HCPELM classifier was trained using the MIAS database and evaluated against benchmark methods. It achieved a breast image recognition accuracy of 86%, outperforming benchmark deep learning models. HCPELM is demonstrating superior performance in early detection and diagnosis, thus aiding healthcare practitioners in breast cancer diagnosis.

Concatenated Modified LeNet Approach for Classifying Pneumonia Images.

Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates a revised Rectified Linear Unit (ReLU) activation function. This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. Batch normalization addresses internal covariate shift, a phenomenon where the distribution of activations within a network alter during training. These modifications help to prevent overfitting and decrease computational time. A comprehensive dataset is used to evaluate the model's performance, and the model is benchmarked against relevant deep-learning models. The results demonstrate a high recognition rate, with an accuracy of 96% in pneumonia image recognition. This research suggests that the Concatenated Modified LeNet classifier has the potential to be a highly useful tool for medical professionals in the diagnosis of pneumonia. By offering accurate and efficient image classification, our model could contribute to improved treatment decisions and patient outcomes.

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis.

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

Comprehensive Analysis of Solar Panel Performance and Correlations with Meteorological Parameters.

To mitigate the adverse effects of fossil fuel-based energy, mankind is in constant search of clean and cost-effective sources of energy, such as solar energy. The economic viability of a power plant to harness solar energy mostly depends on the efficiency of solar panels. Investigations over the years show that the solar panel efficiency significantly depends on the different meteorological parameters. Therefore, there is an imminent need for a correlation explaining the relations between the efficiency and different meteorological parameters. In this study, an effort has been made to analyze the effects of various meteorological parameters on the efficiency and subsequently propose a correlation between them. Initial investigations reveal that the optimal tilt angle for the maximum power output is 26°. The study demonstrates that efficiency is directly proportional to solar intensity and wind speed while being inversely proportional to temperature, humidity, and dew point temperature. Regression analysis of a data set comprising 100 data sets establishes a strong correlation between efficiency and five meteorological parameters: temperature, humidity, wind speed, solar intensity, and dew factor. The calculated efficiencies using the developed correlation deviate from the experimental values, with absolute errors ranging from 0.08 to 1.20%. The findings provided valuable insights for optimizing solar power plant performance by understanding the relationship between efficiency and meteorological parameters.

Tanning Wastewater Sterilization in the Dark and Sunlight Using Psidium guajava Leaf-Derived Copper Oxide Nanoparticles and Their Characteristics.

Employing Psidium guajava (P. guajava) extract from leaves, copper oxide nanoparticles (CuO NPs), likewise referred to as cupric oxide and renowned for their sustainable and harmless biogenesis, have the possibility of being useful for the purification of pollutants as well as for medicinal purposes. The current study examined the generated CuO NPs and their physical qualities by using ultraviolet-visible (UV) spectroscopy. The distinctive peak at 265 nm of the CuO NP production was originally seen. Additionally, an X-ray diffraction (XRD) investigation was conducted to identify the crystalline arrangement of the produced CuO NPs, and a Fourier transform infrared (FTIR) spectroscopy examination was performed to validate the functional compounds of the CuO NPs. Additionally, the synthesized nanoparticles' catalytic activities (wastewater treatment) were analyzed in dark and sunlight modes. The catalytic properties of CuO NPs in total darkness resulted in 64.21% discoloration, whereas exposure to sunshine increased the nanomaterials' catalyst performance to 92.31%. By lowering Cr(VI), Ni, Pb, Co, and Cd in sewage by proportions of 91.4, 80.8, 68.26, 73.25, and 72.4% accordingly, the CuO NP demonstrated its effectiveness as a nanosorbent. Total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biological demand for oxygen (BOD), and conductance were all successfully reduced by nanotreatment of tanning effluents, with proportion reductions of 93.24, 88.62, 94.21, 87.5, and 98.3%, correspondingly.

A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images.

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the "dying ReLU" problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

An investigation of combined effect of infill pattern, density, and layer thickness on mechanical properties of 3D printed ABS by fused filament fabrication.

Additive manufacturing technology and its benefits have a significant impact on different industrial applications. The 3D printing technologies help manufacture lightweight intricate geometrical designs with enhanced strengths. The present study investigates the blended effects of previously recommended parameters of different infill patterns (line, triangle, and concentric) and infill densities (75, 80, and 85%) with varying thicknesses of layers (100, 200, and 300 µm). The test samples were created through Fused Filament Fabrication (FFF) technology using Acrylonitrile Butadiene Styrene (ABS) 3D printing. Mechanical properties were evaluated through tensile and impact strength tests conducted in accordance with ASTM standards. The experimental investigation reveals that the infill pattern greatly affected both tensile and impact strength. The best results were obtained with a concentric infill pattern, along with 80% infill density and 100 µm layer thickness. These conditions resulted in 123% and 115% higher tensile strength and 168% and 80% higher impact strength compared to line and triangle patterns, respectively.

Data Collection and Analysis Methods for Smart Nudging to Promote Physical Activity: Protocol for a Mixed Methods Study.

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people's health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use.