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Epoxy composite coatings are used to reduce friction in mechanical components. The epoxy composites at high temperatures cause serious frictional effects due to poor surface characteristics. Hence, there is a need to find a substitute to improve their frictional behaviour in high-temperature conditions. Adding fillers and lubrication are key solutions to improve tribological properties. In this research work, diglycidyl ether of bisphenol A (DGEBA) epoxy resin was coated on steel, and silica powder (5% by weight) was used as filler. The experiments were conducted under dry and lubricated conditions with and without filler. The coefficient of friction (COF) was examined at different temperatures. The results show that the COF increases with an increase in temperature. In addition, the minimum average COF was observed in the case of epoxy coating with the filler under lubrication conditions. For that case, up to 94.4% decrease in COF was observed as compared to epoxy coating without filler under dry conditions, a 48.33% decrease in COF to epoxy coating without filler under lubricated conditions and a 17.98% decrease in COF than epoxy coating with the filler under dry condition was perceived. Scanning Electron Microscopy (SEM) and surface roughness analyses were also conducted to examine the surface of the specimen after the experiment. A worn and rougher surface was observed in the case of epoxy without filler under dry conditions. The more coefficient of friction at high temperatures is due to the thermal degradation of the epoxy matrix at high temperatures, which results in a rougher surface.

A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.

Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is the best way to handle uncertainties. Our proposed system differentiates new cases provided symptoms of the disease. Generally, it becomes a time-sensitive task to discriminate symptomatic diseases. The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently. Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease. This study aims to differentiate and diagnose COVID-19, Typhoid, Malaria, and Pneumonia. This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms. MATLAB tool is utilised for the implementation of FIS. Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms. The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases. © 2022 Tech Science Press. All rights reserved.

Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.

Disease diagnosis is an exciting task due to many associated factors. Inaccuracy in the measurement of a patient’s symptoms and the medical expert’s expertise has some limitations capacity to articulate cause affects the diagnosis process when several connected variables contribute to uncertainty in the diagnosis process. In this case, a decision support system that can assist clinicians in developing a more accurate diagnosis has a lot of potentials. This work aims to deploy a fuzzy inference-based decision support system to diagnose various diseases. Our suggested method distinguishes new cases based on illness symptoms. Distinguishing symptomatic disorders becomes a time-consuming task in most cases. It is critical to design a system that can accurately track symptoms to identify diseases using a fuzzy inference system (FIS). Different coefficients were used to predict and compute the severity of the predicted diseases for each sign of disease. This study aims to differentiate and diagnose COVID-19, typhoid, malaria and pneumonia. The FIS approach was utilized in this study to determine the condition correlating with input symptoms. The FIS method demonstrates that afflictive illness can be diagnosed based on the symptoms. Our decision support system’s findings showed that FIS might be used to identify a variety of ailments. Doctors, patients, medical practitioners and other healthcare professionals could benefit from our suggested decision support system for better diagnosis and treatment.

In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy individuals. To overcome the class imbalance problem, the synthetic minority oversampling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.

Medical datasets are usually imbalanced, where negative cases severely outnumber positive cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO and NLST, with imbalance ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance of 23 class imbalance methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, etc.), seven over-sampling methods (SMOTE, etc.), and two integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, etc.). The results show that class imbalance learning can improve the classification ability of the model. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable method, and the AUC in the model is generally higher than in other ways. Using ROS, the random forest performs the best predictive ability and is more suitable for the lung cancer datasets used in this study. The code is available at https://mkhushi.github.io/

The widespread human-robot interaction is increasing progressively as robots have made the life of everyone easy-going and comfortable. In this work, we have analysed the behaviour and characteristics of various types of robots. We have also studied the outgrowing relation between robotics and humans. In our analysis, we also have a selection of aspects of this field, which are done by the numerous technologists as well as scientists. We are interested in exploring the functioning of the human brain by generating a functioning system that resolves problems and gives satisfactory results. Artificial intelligence is a vast field that is also pushing its way in the domain of healthcare, business and quality assurance. Various researches disclose that the corporate sector is joining artificial intelligence to estimate the supply-demand concept and automate human resource systems. The public sector is also developing different intelligent machines for security surveillance and malfunction detection of critical systems like nuclear reactors. Artificial intelligence and robotics are also phenomenal to implement the law and order enforcement without any danger. As artificial intelligence is growing, employment in this domain is also increasing due to the high demand of intelligent machines in each sector worldwide. Our primary focus is to delve into the relationship between humans and robots.

The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random undersampling and Synthetic Minority Over Sampling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random undersampling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.

Financial threats are displaying a trend about the credit risk of commercial banks as the incredible improvement in the financial industry has arisen. In this way, one of the biggest threats faces by commercial banks is the risk prediction of credit clients. Recent studies mostly focus on enhancing the classifier performance for credit card default prediction rather than an interpretable model. In classification problems, an imbalanced dataset is also crucial to improve the performance of the model because most of the cases lied in one class, and only a few examples are in other categories. Traditional statistical approaches are not suitable to deal with imbalanced data. In this study, a model is developed for credit default prediction by employing various credit-related datasets. There is often a significant difference between the minimum and maximum values in different features, so Min-Max normalization is used to scale the features within one range. Data level resampling techniques are employed to overcome the problem of the data imbalance. Various undersampling and oversampling methods are used to resolve the issue of class imbalance. Different machine learning models are also employed to obtain efficient results. We developed the hypothesis of whether developed models using different machine learning techniques are significantly the same or different and whether resampling techniques significantly improves the performance of the proposed models. One-way Analysis of Variance is a hypothesis-testing technique, used to test the significance of the results. The split method is utilized to validate the results in which data has split into training and test sets. The results on imbalanced datasets show the accuracy of 66.9% on Taiwan clients credit dataset, 70.7% on South German clients credit dataset, and 65% on Belgium clients credit dataset. Conversely, the results using our proposed methods significantly improve the accuracy of 89% on Taiwan clients credit dataset, 84.6% on South German clients credit dataset, and 87.1% on Belgium clients credit dataset. The results show that the performance of classifiers is better on the balanced dataset as compared to the imbalanced dataset. It is also observed that the performance of data oversampling techniques are better than undersampling techniques. Overall, the Gradient Boosted Decision Tree method performs better than other traditional machine learning classifiers. The Gradient Boosted Decision Tree method gives the best results while utilizing the K-means SMOTE oversampling method. Using one-way ANOVA, the null hypothesis was rejected by a p-value

Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations. The significance of deep learning, a subset of artificial intelligence in the area of medical diagnosis applications, has macadamized the path in rapid developments for brain tumor detection from MRI to higher prediction rate. For brain tumor analysis and classification, the convolution neural network (CNN) is the most extensive and widely used deep learning algorithm. In this work, we present a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the brain. Pretrained models are demonstrated on the MRI brain tumor images dataset consisting of 233 images. Our paper aims to locate brain tumors with the utilization of the VGG-16 pretrained CNN model. The performance of our model will be evaluated on accuracy. As an outcome, we can estimate that the pretrained model VGG-16 determines highly adequate results with an increase in the accuracy rate of training and validation.

The Internet of Things (IoT) is the new appearing technology and produces big data nowadays. Today, IoT is primarily a vision that dictates that everything should be interconnected over the Internet. IoT is currently and for sure will be the foundation for future development because it brings new opportunities for novel services. The IoT industry is flourishing since the number of computations that a computer can execute almost doubles biyearly. In contrast, the size and the amount of power needed are nearly half for the same period. This means that smaller and more powerful devices are available for interconnection and data exchange offering a wide range of applications. This aspect leads to significant security challenges, which will be treated from now on. IoT is being used in numerous applications like health care, home automation, smart car, industrial automation, etc. IoT features bring convenience to us, but threats also emerge with them. It shows plenty of security troubles like securing these gadgets, information and correspondence from unapproved to access these gadgets. To enable analysts to pursue the forward works in this paper, what are the security challenges the IoT features face, what solutions are provided yet, and the open challenges.

An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling.

Today decision-making and business planning are fundamental challenges, especially for retailers. Vendors are paying more attention to find out the age group of customers most affected by and interested in special offers on events such as Blessed Friday (falsely known as Black Friday). Besides, they want to know which strategy or marketing plan should be adopted to grasp more customers. Therefore, to assess customers’ needs or figure out the group more interested in different Blessed Friday deals is challenging. Many solutions to this problem, like analysis of Blessed Friday consumer behaviour, classification of retail stores, etc., have been proposed. Still, such solutions do not consider the age group of customers. In this research, a comparison of techniques for predicting the age group of shoppers on a Blessed Friday is proposed based on machine learning. This work is done on a dataset obtained from a repository published online. During pre-processing of the dataset, missing values are replaced. Algorithms such as Decision Tree, K-Nearest Neighbor, Naive Bayes, and Neural Network are applied to determine the most suitable algorithm. This study provides a good understanding of classification, targets the age group more interested in Blessed Friday, and provides primary research ground for further analysis in this domain.

Anomaly detection is a significant problem that has been studied in a broader spectrum of research areas due to its diverse applications in different domains. Despite the usage of modern technologies and the advances in system monitoring and anomaly detection techniques, false-positive rates are still high. There exist many anomaly detection algorithms, among them few are domain-specific, and others are more generic techniques. Despite a significant amount of advance in this research area, there does not exist a single winning anomaly detector known to work well across different datasets. In this paper, we review the literature related to types of anomalies, data types of anomalies, data types of time-series, components of time-series data, classification of anomalies context, and classification methods of time-series anomalies detection. We presented a taxonomy to characterize the various aspects related to time-series anomaly detection. One of the key challenges in current anomaly detection techniques is to perform anomaly detection with regards to the type of activities or the context that a system is exposed. We hope that this investigation gives a more remarkable ability to understand the evolving methods of time-series anomaly detection and how computational methods can be applied in this domain in the future.

In a wireless sensor network (WSN), node localization is a key requirement for many applications. The concept of mobile anchorbased localization is not a new concept; however, the localization of mobile anchor nodes gains much attention with the advancement in the Internet of things (IoT) and electronic industry. In this paper, we present a range-free localization algorithm for sensors in a three-dimensional (3D) wireless sensor networks based on flying anchors. The nature of the algorithm is also suitable for vehicle localization as we are using the setup much similar to vehicle-to-infrastructure- (V2I-) based positioning algorithm. A multilayer C-shaped trajectory is chosen for the random walk of mobile anchor nodes equipped with a Global Positioning System (GPS) and broadcasts its location information over the sensing space. The mobile anchor nodes keep transmitting the beacon along with their position information to unknown nodes and select three further anchor nodes to form a triangle. The distance is then computed by the link quality induction against each anchor node that uses the centroid-based formula to compute the localization error. The simulation shows that the average localization error of our proposed system is 1.4 m with a standard deviation of 1.21 m. The geometrical computation of localization eliminated the use of extra hardware that avoids any direct communication between the sensors and is applicable for all types of network topologies. V2X-Based Mobile Localization in 3D Wireless Sensor Network

Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

Ovarian cancer is a fatal female reproductive cancer because it has no specific clinical manifestations and effective screening methods in the early stage. When it is discovered, it is already an advanced stage with a low cure rate. Therefore, it is of great significance to improve the diagnostic ability of early screening for ovarian cancer. In this study, we feature engineered CA125 by calculating the rate of change of CA125 in addition to selecting a few top-ranked important features from PLCO ovarian cancer dataset. The dataset was extremely imbalanced; the imbalance ratio (ratio of negative samples in the majority class to positive examples in the minority class) was 143.7. Twenty-three types of class-imbalanced learning methods were used in this study to improve the predictive ability of the model. We identified the decision tree method with the highest AUC value among nine classic classifiers (decision tree, AdaBoost, etc.) to build a model with the class imbalance method, and showed their comparison. We identified the decision tree using SVMSMOTE has the most robust predictive ability for ovarian cancer, with a PPV of 0.9041, AUC of 0.9532, the sensitivity of 0.7792, and specificity of 0.9982. The high PPV shown by the model selected in this study indicates that the true positive samples predicted by the model account for 90.41%. Compared with other studies, the PPV of this study increased by 81.3%. This study helps to improve the accuracy of early screening for ovarian cancer and makes the diagnosis of ovarian cancer more reliable.

The year 2020 has changed the living style of people all around the world. Corona pandemic has affected the people in all fields of life economically, physically, and mentally. This dataset is a collection of published articles discussing the effect of COVID and SARS on the social sciences from 2003 to 2020. This dataset collection and analysis highlight the significance and influential aspects, research streams, and themes in this domain. The analysis provides top journals, highly cited articles, mostly used keywords, top affiliation institutes, leading countries based on the citation, potential research streams, a thematic map, and future directions in this area of research. In the future, this dataset will be helpful for every researcher and policymakers to proceed as a starting point to identify the relevant research based on the analysis of 18 years of research in this domain.

Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region.

The present-day world has become all dependent on cyberspace for every aspect of daily living. The use of cyberspace is rising with each passing day. The world is spending more time on the Internet than ever before. As a result, the risks of cyber threats and cybercrimes are increasing. The term ‘cyber threat’ is referred to as the illegal activity performed using the Internet. Cybercriminals are changing their techniques with time to pass through the wall of protection. Conventional techniques are not capable of detecting zero-day attacks and sophisticated attacks. Thus far, heaps of machine learning techniques have been developed to detect the cybercrimes and battle against cyber threats. The objective of this research work is to present the evaluation of some of the widely used machine learning techniques used to detect some of the most threatening cyber threats to the cyberspace. Three primary machine learning techniques are mainly investigated, including deep belief network, decision tree and support vector machine. We have presented a brief exploration to gauge the performance of these machine learning techniques in the spam detection, intrusion detection and malware detection based on frequently used and benchmark datasets.

A significant type of Lung cancer today know to the world is called Malignant Mesothelioma (MM). MM is associated with an inferior prognosis, and the majority of patients do not show symptoms. The etiology of MM is essential for the identification of disease. Clinical results provide a better way for the treatment of disease. Typically, costly imaging and laboratory resources, i.e. (X-rays, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, biopsies, and blood tests) are required for the identification of MM risk factors. Furthermore, these methods are often expensive and invasive. The primary purpose of this work is to explore risk factors of MM. The dataset consists of healthy and mesothelioma patients, but only mesothelioma patients were selected for the identification of symptoms. The raw data set has been pre-processed, and then the Apriori method was utilized for association rules with various configurations. The pre-processing task involved the removal of duplicated and irrelevant attributes, balanced the dataset, numerical to the nominal conversion of attributes in the dataset and creating the association rules in the dataset. Strong associations of disease's factors; asbestos exposure, erythrocyte sedimentation rate, duration of time for asbestos exposure and Pleural to serum LDH ratio determined via Apriori algorithm. The identification of risk factors associated with MM may prevent patients from going into the high danger of the disease. This will also help to control the comorbidities associated with MM, which are cardiovascular diseases, cancer-related emotional distress, diabetes, anemia, and hypothyroidism.

A throughput is a measure of successful data transmission through a terminal in a given time. There is a significant number of surveys that have been published to measure the performance of MAC protocols and each MAC channel. There is a substantial need to perform a comparative analysis of achieved results that can provide a comprehensible result for the end-users. In our work, we have applied different methods to obtain the throughput of contention-free and contention-based protocols. We have provided a brief comparison between different protocols of the same class to accentuate the efficient protocols. Our paper incorporates collision avoidance MAC channels, as the collision avoidance techniques provide better usability of protocols than the collision detection techniques. We have also proposed an efficient way to increase the life span of the Low-energy Adaptive Clustering Hierarchy (LEACH) protocol using Time Division Multiple Access (TDMA). The goal to obtain the maximum effectiveness of a network can be achieved by maximal utilization of the throughput. In this paper, we have provided a brief comparison of different MAC protocols by using the throughput parameter, and we have also proposed a new method to increase the efficiency of LEACH protocol.