Recent Advances in Computer Science and Communications - Volume 15, Issue 6, 2022

IoT becomes more complicated due to its large size. The existing techniques of Wireless Sensor Networks (WSN) are not useful directly to the IoT; that is why using energyefficient schemes for the IoT is a challenging issue. Due to battery-constrained IoT devices, energy efficiency is of great importance. This paper gives an overview and broad survey on IoT, WSN in IoT, challenges in IoT and WSN, energy-conserving issues and solutions and different Node Deployment patterns. This paper addresses energy competence issues for green IoT by proposing an energy-efficient heuristic for a regular and particular deployment scheme. QCMCSC heuristic is implemented for Strip Based Deployment Pattern and analyzed in terms of Energy Efficiency and Life Time of a sensor on Energy Latency Density Design Space, a topology management application that is power efficient. QC-MCSC for Strip-based deployment patterns and random deployment patterns are compared.

Introduction: The goal of this study is to expand the diabetes decision-making framework through the advancement of computational intelligence. Several artificial network and machine- learning-based methods have been developed and validated, most of which are based on the Pima Indian dataset. So far, no method has reached an accuracy of 99-100%. Various tools such as Machine Learning (ML) and Data Mining are used for the correct identification of diabetes. These tools improve the diagnostic process associated with T2DM. Diabetes mellitus type 2 (DMT2) is a major problem in several developing countries, and its early diagnosis can save several people’s lives. Accordingly, we have to build a structure that diagnoses type 2 diabetes. This paper proposes a fuzzy expert system that uses the Mamdani fuzzy inference structure (MFIS) to diagnose type 2 diabetes accurately. The proposed research work has been created using a variety of machine learning algorithms such as J48 Decision-tree (DT), Multilayer perceptron (MLP), Support-vector-machine (SVM), Naive-Bayes (NB), Fusion, and Mixed fusion-based. Actual data from the UCI machine learning datasets are used to validate the advanced Fuzzy expert system (FES) and machine learning algorithms. Objective: A review of recent advances in machine learning-based classification models for diabetes diagnosis is presented in this survey paper. Methods: This paper compares modified fusion processes to fundamental models such as radial basis function, K-nearest neighbor, support vector machine, J48, logistic regression, classification, regression trees, etc., for diagnosing type 2 diabetes. Results: Figs. 3 and 4 show the results for each classifier based on prediction accuracy. Conclusion: The fuzzy expert system is the best among its rival classifiers. SVM performs very poorly with a very low true positive rate, i.e., a very high number of positive cases misclassified as (non-diabetic) negative. Based on the evaluation, it is clear that the fuzzy expert system has the highest precision value. However, J48 is the least accurate classifier. Compared to the other classifiers listed in the testing section, it has the greatest number of false positives. The results show that the fuzzy expert system has the uppermost cost for both precision and recall. Thus, it has the uppermost value for F-measure in the training and testing datasets. J48 is considered the secondbest classifier for the training dataset, whereas Naïve Bayes comes in the second rank in the testing dataset.

Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the prevailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and depression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease. Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression. Results: Women, in general, were more stressed and depressed than men . Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and female students. The overall analysis showed that the people of India were stressed and depressed at “Serve” level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as well as their salary and aged people were depressed due to their health concerns in COVID-19 disaster . Conclusion: The researchers conducted an analysis of data based on DASS-21 parameters defined for anxiety, depression, and stress at the global level. By the analysis defined in section 5, researchers concluded that the people of India are more stressed and depressed at "Serve" level due to COVID-19.

Introduction: In developing safety and security-critical systems, separation kernel acts as a primary foundation, which provides spatial as well as temporal separation. The separation kernel offers highly assured partitions to the applications hosted on the fundamentally critical systems and can also control the flow of information between them. The industries, as well as academia, have developed several separation kernels that have been broadly applied in critical systems like military/defense secured applications, avionics/aerospace intelligent systems, healthcare units that deal with human lives and in many more areas. The increasing popularity of separation kernels demands the formal verification that assures the correctness of the functionalities in it. Further, formal verification of separation kernels has become mandatory by the security/safety certification authorities. Conclusion: This paper first presents the concept of the separation kernel, and then it discusses the functionalities, design, and properties of it. The classification and analysis of the formal languages are being presented in this paper used for writing the specifications of the separation kernel and verifying it. The paper is an attempt towards the classification of formal languages being used for the verification of several separation kernels.

Background: Unmanned aerial vehicle automatic fault identification of high voltage transmission equipment has entered the stage of product development, in which image recognition technology is one of the key technologies. There are often bird nests on the high voltage transmission tower, which have an impact on the transmission, so they need to be automatically detected. Methods: For bird's nest recognition, a novel algorithm is proposed. Firstly, the template image and auxiliary function are used to construct the system, and the iterative point trajectory set, called feature set, is obtained by iteration; Then, the target image is searched by blocks, and the image blocks are iterated with the same auxiliary function to construct the iterative system, and the set of iterative point tracks to be identified is obtained. The correlation coefficient is calculated by comparing the feature set with those to be recognized. And we can confirm whether the image block is a bird's nest according to the size of the correlation coefficient. Results: Different from the general image recognition method, the iterative algorithm obtains the iterative trajectory by iterating the image and the auxiliary function, and takes the iterative trajectory as the image feature, then the feature comparison is carried out, so as to achieve the goal of bird's nest recognition. The effectiveness of the method is proved by experiments. The recognition accuracy is 99% by experiment on the self-built data set. Conclusion: This paper proposes a new feature extraction algorithm for bird's nest recognition. The algorithm based on iteration is very simple and effective for bird's nest identification. As a new method, it needs further development and improvement.

Background: Humanity today faces a global emergency. It is conceivably the greatest crisis of our generation. The coronavirus pandemic, which has many global implications, has led researchers worldwide to seek solutions to this crisis, including the search for effective treatment in the first place. Objective: This study aims to identify the factors that can have an essential effect on COVID-19 comportment. Having proper management and control of imports of COVID-19 depends on many factors that are highly dependent on a country's sanitary capacity and infrastructure technology. Nevertheless, meteorological parameters can also be a connecting factor to this disease; since temperature and humidity are compatible with a seasonal respiratory virus's behavior. Method: In this work, we analyze the correlation between weather and the COVID-19 epidemic in Casablanca, the economic capital of Morocco. It is based on the primary analysis of COVID-19 surveillance data from the Ministry of Health of the Kingdom of Morocco and weather data from the meteorological data. Weather factors include minimum temperature (°C), maximum temperature (°C), mean temperature (°C), maximum wind speed (Km/h), humidity (%), and rainfall (mm). The Spearman and Kendall rank correlation test is used for data analysis. Between the weather components. Results: The mean temperature, maximum temperature (°C) and Humidity were significantly correlated with the COVID-19 pandemic with respectively (r= -0.432, r = -0.480; r=0.402, and p=- 0.212, p= -0.160, and p= -0.240). Conclusion: This discovery helps reduce the incidence rate of COVID-19 in Morocco, considering the significant correlation between weather and COVID-19, of about more than 40%.

Objective: Defects in delivered software products not only have financial implications but also affect the reputation of the organisation and lead to wastage of time and human resources. This paper aims to detect defects in software modules. Methods: Our approach sequentially combines SMOTE algorithm with K - means clustering algorithm to deal with class imbalance problem to obtain a set of key features based on the interclass and intra-class coefficient of correlation and ensemble modeling to predict defects in software modules. After cautious examination, an ensemble framework of XGBoost, Decision Tree, and Random Forest is used for the prediction of software defects owing to numerous merits of the ensembling approach. Results: We have used five open-source datasets from NASA PROMISE repository for software engineering. The result obtained from our approach has been compared with that of individual algorithms used in the ensemble. A confidence interval for the accuracy of our approach with respect to performance evaluation metrics, namely accuracy, precision, recall, F1 score and AUC score, has also been constructed at a significance level of 0.01. Conclusion: Results have been depicted pictographically.

Background: In a parallel processor, the pipeline cannot fetch the conditional instructions with the next clock cycle, leading to a pipeline stall. Therefore, conditional instructions create a problem in the pipeline because the proper path can only be known after the branch execution. To accurately predict branches, a significant predictor is proposed for the prediction of the conditional branch instruction. Methods: In this paper, a single branch prediction and a correlation branch prediction scheme are applied to the different trace files by using the concept of saturating counters. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. Results: Firstly, a single branch prediction and correlation branch prediction technique are applied to the trace files using saturating counters. By comparison, it can be observed that a correlation branch prediction technique provides better results by enhancing the accuracy rate of 2.25% than the simple branch prediction. Further, a hybrid branch prediction scheme is proposed, which uses both global and local branch information, providing more accuracy than the single and correlation branch prediction schemes. The results suggest that the proposed hybrid branch prediction schemes provide an increased accuracy rate of 3.68% and 1.43% than single branch prediction and correlation branch prediction. Conclusion: The proposed hybrid branch prediction scheme gives a lower misprediction rate and higher accuracy rate than the simple branch prediction scheme and correlation branch prediction scheme.

Background: Electricity consumption prediction plays an important role in conservation, development, and future planning. Accurate prediction model has various field applications in real-life scenarios, future electricity demand estimation, performance evaluation of current time, fault detection, efficient energy production, resource-saving, and many more. In this paper, a CNN based short term building electricity consumption prediction model is developed and tested for two different types of datasets that can perform weekly prediction. Two different datasets are used to check how the algorithm behaves on different datasets i.e., what are the impacts dataset has on prediction accuracy. Errors were calculated using MAE and RMSE. Objective: The objective of the study is to develop an electricity consumption prediction (ECP) model for a univariate and multivariate dataset using CNN and LSTM network and to find that how the correlation and independency of features affect the electricity prediction task. Methods: The proposed electricity consumption model is built using the deep CNN andLSTM network and is trained and tested using the univariate and multivariate time series dataset thus the two experiments have been performed and are named as U-ECPCL (Univariate- Electricity Consumption Prediction using CNN and LSTM) and M-ECPCL (Multivariate- Electricity Consumption Prediction using CNN and LSTM) respectively. Results: The model predicts accurately with few errors with MAE of 0.251 and RMSE of 0.66 for univariate dataset and MAE of 4.36 and RMSE of 11.53 for a multivariate dataset. Conclusion: The model predicts accurately with few errors and if the prediction error of univariate and multivariate are compared then it is concluded that the univariate model outperforms the multivariate model.