Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City

The world is rapidly changing with the advance of information technology.The expansion of the Internet of Things(IoT)is a huge step in the development of the smart city.The IoT consists of connected devices that transfer information.The IoT architecture permits on-demand services to a public pool of resources.Cloud computing plays a vital role in developing IoT-enabled smart applications.The integration of cloud computing enhances the offering of distributed resources in the smart city.Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability,security,performance,condentiality,and privacy.The key reason for cloud-and IoT-enabled smart city application failure is improper security practices at the early stages of development.This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications.Its three-layered architecture includes privacy preserved stakeholder analysis(PPSA),security requirement modeling and validation(SRMV),and secure cloud-assistance(SCA).A case study highlights the applicability and effectiveness of the proposed framework.A hybrid survey enables the identication and evaluation of signicant challenges.

Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network(ANN)

The performance of correlation between the dielectric parameters of Baobab Oil(BAO)and Mongongo Oil(MGO)is evaluated using Artificial Neural Network(ANN).The BAO and MGO naturally own high Unsaturated Fatty Acids(UFAs)and are highly biodegradable.The temperature studies and dielectric studies are carried out and found that the Natural Esters(NEs)show a reliable performance over mineral oil-based Transformer Oil(TO).Further the endurance test,Partial Discharge Inception Voltage(PDIV)repetition rate and drop after 30 days,dielectric measurements are done as per the standards of IEC(International Electrotechnical Commission)and ASTM(American Society for Testing and Materials).The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO.The C10H22 or Kerosene(KER)and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation.The C10H22 acts as a thinning agent and reduces the ageing rate of the NEs,and this leads to slower rate of water saturation.This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90°C shows better strength of liquid insulation.The performance of association between the dielectric properties like breakdown voltage and water content,dissipation factor and thermal conduc-tivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.