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.

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives.It results in illegal access to users’private data and compromises it.Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data.Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity.This article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)model.The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing.The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word elimination.Besides,TF-IDF model is applied for the extraction of useful feature vectors.Moreover,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process.The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions.Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

Optimal Weighted Extreme Learning Machine for Cybersecurity Fake News Classification

Fake news and its significance carried the significance of affecting diverse aspects of diverse entities,ranging from a city lifestyle to a country global relativity,various methods are available to collect and determine fake news.The recently developed machine learning(ML)models can be employed for the detection and classification of fake news.This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine(CAS-WELM)for Cybersecurity Fake News Detection and Classification.The goal of the CAS-WELM technique is to discriminate news into fake and real.The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embed-ding process.Then,N-gram based feature extraction technique is derived to gen-erate feature vectors.Lastly,WELM model is applied for the detection and classification of fake news,in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm.The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions.The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

Exploration of IoT Nodes Communication Using LoRaWAN in Forest Environment

The simultaneous advances in the Internet of Things(IoT),Artificial intelligence(AI)and Robotics is going to revolutionize our world in the near future.In recent years,LoRa(Long Range)wireless powered by LoRaWAN(LoRa Wide Area Network)protocol has attracted the attention of researchers for numerous applications in the IoT domain.LoRa is a low power,unlicensed Industrial,Scientific,and Medical(ISM)bandequipped wireless technology that utilizes a wide area network protocol,i.e.,LoRaWAN,to incorporate itself into the network infrastructure.In this paper,we have evaluated the LoRaWAN communication protocol for the implementation of the IoT(Internet of Things)nodes’communication in a forest scenario.The outdoor performance of LoRa wireless in LoRaWAN,i.e.,the physical layer,has been evaluated in the forest area of Kashirampur Uttarakhand,India.Hence,the present paper aims towards analyzing the performance level of the LoRaWAN technology by observing the changes in Signal to Noise Ratio(SNR),Packet Reception Ratio(PRR)and Received Signal Strength Indicator(RSSI),with respect to the distance between IoT nodes.The article focuses on estimating network lifetime for a specific set of LoRa configuration parameters,hardware selection and power constraints.From the experimental results,it has been observed that transmissions can propagate to a distance of 300 m in the forest environment,while consuming approx.63%less energy for spreading factor 7 at 2 dBm,without incurring significant packet loss with PRR greater than 80%.

Big Data Analytics Using Swarm-Based Long Short-Term Memory for Temperature Forecasting

In the past few decades,climatic changes led by environmental pollution,the emittance of greenhouse gases,and the emergence of brown energy utilization have led to global warming.Global warming increases the Earth’s temperature,thereby causing severe effects on human and environmental conditions and threatening the livelihoods of millions of people.Global warming issues are the increase in global temperatures that lead to heat strokes and high-temperature-related diseases during the summer,causing the untimely death of thousands of people.To forecast weather conditions,researchers have utilized machine learning algorithms,such as autoregressive integrated moving average,ensemble learning,and long short-term memory network.These techniques have been widely used for the prediction of temperature.In this paper,we present a swarm-based approach called Cauchy particle swarm optimization(CPSO)to find the hyperparameters of the long shortterm memory(LSTM)network.The hyperparameters were determined by minimizing the LSTM validationmean square error rate.The optimized hyperparameters of the LSTM were used to forecast the temperature of Chennai City.The proposed CPSO-LSTM model was tested on the openly available 25-year Chennai temperature dataset.The experimental evaluation on MATLABR2020a analyzed the root mean square error rate and mean absolute error to evaluate the forecasted output.The proposed CPSO-LSTM outperforms the traditional LSTM algorithm by reducing its computational time to 25 min under 200 epochs and 150 hidden neurons during training.The proposed hyperparameter-based LSTM can predict the temperature accurately by having a root mean square error(RMSE)value of 0.250 compared with the traditional LSTM of 0.35 RMSE.