Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms

Sustainable Agriculture Practice using Machine Learning

The changing climate has caused unpredictable rainfall, unusual temperature drops, and heat waves, leading to considerable damage to the environment. Fortunately Machine Learning has provided effective tools to address global issues, including agriculture. By employing different ML algorithms, it is possible to solve the agricultural problems caused by these climate changes. The objective of this article is to develop a system for crop recommendation and disease detection in a plant. Publicly available datasets were used for both tasks. For the crop recommendation system, feature extraction was performed, and the dataset was trained using various Machine Learning algorithms, namely Decision Tree, Logistic Regression, Random Forest, Support Vector Machine (SVM) and Multilayer Perceptron. The random forest algorithm achieved an excellent accuracy of 99.31%.For the plant disease identification system, CNN architectures like - VGG16, ResNet50, and EfficientNetV2 - were trained and compared. Among these, EfficientNetV2 achieved high accuracy of 96.07%

DEVELOPMENT AND VALIDATION OF A RP-HPLC METHOD FOR ESTIMATION OF TELMISARTAN IN HUMAN PLASMA

Objective: The present study was aimed to develop a rapid, specific and sensitive method based on high performance liquid chromatographic method was developed for the determination of telmisartan using indapamide as an internal standard.Methods: The utilization of single step protein precipitation method using methanol as a precipitating agent becomes suitable for analysis of a large number of samples. The developed method was validated as per US-FDA guidelines for telmisartan in human plasma.Result: An isocratic separation was achieved using Hibar C18 (250 x 4.6 mm, 5 μm) column using 10 mmol ammonium formate solution (pH 4.0)–methanol (70:30, v/v) as the mobile phase. Detection was carried out at 275 nm. The method was validated over the range of 0.1–1.5 µg/ml in human plasma with a regression analysis of 0.996. The percentage recovery of the present method was found to be 94.0–99.2 %.Conclusion: The developed analytical method was found to be rapid, single step, plasma preparation coupled with the simple high-performance liquid chromatography coupled with UV detection (HPLC–UV) isocratic chromatographic apparatus makes the method cost-effective and suitable for analysis of a large number of samples

Investigation of magnetic and structural properties of copper substituted barium ferrite powder particles via co-precipitation method

AbstractIn this paper, it is proposed to synthesize copper-doped barium ferrite (BaCuxFe12−xO19) using co-precipitation technique at different ratios in order to increase the coercivity value which in turn increases the magnetic storage capacity of the copper-doped barium ferrite powder. This technique is very compactable with lower cost. This method was used to prepare different ratio (0–8%) of copper-doped barium ferrite samples exposed to sintering process under the temperature 1200°C for 6h because the base and doped materials are combined to form a new compound. The sintered compound is involved the XRD analysis and the obtained values are matched with the constant standards (a=b=5.864Å and c=23.098Å). Hence, the samples proved as hexagonal system. TGA/DTA used to establish selected characteristics of materials that exhibit decomposition and oxidation process. FT-IR spectroscopy used to confirm the chemical bonds and vibration mode of samples. Using beam of X-rays, XPS spectra obtained and the binding energy of the sample is measured. From the SEM analysis, the morphology and grain size of the copper-doped barium ferrite powder materials are found. The vibrating sample magnetometer measures the magnetic saturation, magnetic reminisce and coercivity of a sample. The magnetic saturation, magnetic remanence and coercivity values are found and tabulated

Detection and classification of breast cancer types using VGG16 and ResNet50 deep learning techniques

Breast cancer has become a major worldwide health issue, accounting for a large portion of the mortality rate among women. As a result, the need for early detection techniques to enhance prognosis is increasing. Many techniques are being used to detect breast cancer early, and treatment outcomes are frequently favorable when the disease is detected early. Mammography is a commonly used and very successful method for identifying breast cancer among these modalities. Through additional image processing operations like resizing and normalizing, this technology allows the detection of malignant spots from mammography pictures of the affected area. The goal of our research is to improve breast cancer detection and diagnosis speed and accuracy. In this study, we investigate the use of deep learning methods, particularly the visual geometry group (VGG16) and ResNet50 models, for mammography-based breast cancer detection. We assess the performance of the VGG16 and ResNet50 models by training and testing on a mammogram dataset that consists of 322 images from the mammographic image analysis society (MIAS) dataset. The suggested models aim to classify these images into normal, benign, and malignant groupings. Our results show better performance when compared to existing approaches. The proposed methods VGG16 and ResNet50 show promising results, achieving a classification accuracy of 91.23% and 99.01% respectively

Machine Learning Algorithm Based Bandwidth Prediction for Internet Usage

The applicability of framework structure and affiliation arranging recognize a basic activity in the bandwidth prediction. The procedure for predicting the framework use is to see the basic transmission limit with respect to future periods. This prediction helps with utilizing the techniques workplaces in the saint way. Thinking about the fundamental cost of bandwidth, at top hours of a framework traffic we can follow an amazing sort of plan to purchase. In this paper, the past use data of FWDR organize centers is at risk to univariate direct time plan ARIMA model after precise change is used to calculate necessary bandwidth limit concerning future needs. The anticipated data is veered from the obvious data gained from a for all intents and purposes indistinguishable framework and the foreseen data has been viewed as inside ten percent MAPE. This design reduction the MAPE by eleven point seventy-one percentage and fifteen point forty-two percent of self-rulingly when stood separated from the non-able changed ARIMA model at ninety-nine percent CI. The outcome show that the suitably changed ARIMA design has improved show when meandered from non-intentionally changed ARIMA model. Increasingly significant dataset can be passed on with season alterations and thought of expanded length groupings, for dynamically unequivocal and longer term needs.</jats:p

Minimizing Energy Consumption Based on Neural Network in Clustered Wireless Sensor Networks

Wireless sensor networks were organized with the collections of sensor nodes for the purpose of monitoring physical phenomenon such as temperature, humidity and seismic events, etc., in the real world environments where the manual human access is not possible. The major tasks of this type of networks are to route the information to sink systems in the sensor network from sensor nodes. Sensors are deployed in a large geographical area where human cannot enter such as volcanic eruption or under the deep sea. Hence sensors are not rechargeable and limited with battery backup; it is very complicated to provide the continuous service of sending information to sink systems from sensor nodes. To overcome the drawback of limited battery power, this paper proposes the concept of minimizing energy consumption with the help of neural networks. The modified form of HRP protocol called energy efficient HRP protocol has been implemented in this paper. Based on this concept, the workload of cluster head is shared by the cluster isolation node in order to increase the lifetime of the cluster head node. Also cluster monitoring node is introduced to reduce the re-clustering process. The implementation procedure, algorithm, results and conclusions were proved that the proposed concept is better than the existing protocols.</jats:p