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It is already certificated that collaborative filtering algorithms could alleviate such data sparsity and long tail distribution problems and provide high performance in item recommendation. However, high computational complexity and insufficient samples may lead to low convergence and inaccuracy in traditional recommender approaches. In this article, a novel deep neural network-based collaborative filtering recommender engine referred to as NtCF is proposed, which resorts to a neural architecture for preference learning and user representation. With the powerful capability of neural network, NtCF is able to deep exploit interactions within social network for each user. More specifically, the trust-aware attention layer is designed to indicate the social influence to each user; furthermore, NtCF performs item clustering via k-means++ and conducts item recommendation within each generated item cluster, and accordingly, NtCF can achieve significant improvement in recommendation performance and provide hierarchical recommendation services. In practice, experimental comparison over three real-world datasets also demonstrates the superiority of NtCF in contrast to state-of-the-art recommender approaches, which can achieve high performance in top-N recommendation and provide much better user experience.

Recent researches indicate that pairwise learning to rank methods could achieve high performance in dealing with data sparsity and long tail distribution in item recommendation, although suffering from problems such as high computational complexity and insufficient samples, which may cause low convergence and inaccuracy. To further improve the performance in computational capability and recommendation accuracy, in this article, a novel deep neural network based recommender architecture referred to as PDLR is proposed, in which the item corpus will be partitioned into two collections of positive instances and negative items respectively, and pairwise comparison will be performed between the positive instances and negative samples to learn the preference degree for each user. With the powerful capability of neural network, PDLR could capture rich interactions between each user and items as well as the intricate relations between items. As a result, PDLR could minimize the ranking loss, and achieve significant improvement in ranking accuracy. In practice, experimental results over four real world datasets also demonstrate the superiority of PDLR in contrast to state-of-the-art recommender approaches, in terms of Rec@N, Prec@N, AUC and NDCG@N.

The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.

On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation.

Breast cancer is one the most critical disease and suffered many people around the world. The efficient and correct detection of breast cancer is still needed to ensure this medical issue although the researchers around the world are proposed different diagnostic methods for detection of this disease, however these existing methods still needed further improvement to correct and efficient detection of this disease. In this study, we proposed a new breast cancer identification method by using machine learning algorithms and clinical data. In the proposed method supervised (Relief algorithm) and unsupervised (Autoencoder, PCA algorithms) techniques have been used for related features selection from data set and then these selected features have been used for training and testing of classifier support vector machine for accurate and on time detection of breast cancer. Additionally, in the proposed approach k fold cross validation method has been used for model validation and best hyperparameters selection. The model performance evaluation metrics have been used for model performance evaluation. The BC data sets have been used for testing of the proposed method. The analysis of experimental results has been demonstrated that the features selected by Relief algorithm are more related for accurate detection of Breast cancer instead of features selected by Auotencoder and PCA algorithms. The proposed method has been attained high results in terms of accuracy on selected feature selected by Relief algorithm and achieved 99.91% accuracy. We have been employed McNemar’s statistical test for performance comparison of our different models. Further, the proposed method performance has been compared with baseline methods in the literature and the proposed method performance is high as compared to base line methods. Due to the high performance of the proposed method (Relief-Support vector machine) we highly recommended it for the diagnosis of breast cancer. In addition, the proposed method can be easily incorporated into the healthcare system for reliable diagnosis of Breast cancer.

In the 21st century, transportation brought great convenience to people, but at the same time, automobile transportation is the major factor causing greenhouse gas emissions and climate change. Movements of the world towards green environments, there is hike in use and production of electric vehicles (energy vehicles). However, with the continuous growth in the number of energy vehicles, it is necessary for the government to provide strong support in the construction of charging piles. Real-time and effective management has become a practical problem for the relevant departments which needs to be solved. This paper uses the information research method to fuse the huge amount of heterogeneous data generated by the charging pile resultant to the new energy electric vehicle in the vehicle network and introduces cloud computing as its storage module to facilitate the storage and related expansion of the big data. This paper proposes a system scheme of heterogeneous data fusion based on cloud computing for the acquisition, storage, and fusion of heterogeneous data in the vehicle network. After testing the results, it shows that the system is stable and effective in practical application, which can meet the design requirements of the system. What is the significance of analyzing big data of charging point? Considering from the supply side, obtaining the user’s charging behaviour data is helpful to build a digital map of the charging pile of new energy vehicles, connect the service information between the vehicle enterprises and the charging pile enterprises, and provide the most comprehensive and effective real-time charging information covering the widest range of vehicles, which can solve many problems of information asymmetry in the current charging information service.

The Internet of Things is made of diverse networked things (i.e., smart, intelligent devices) that are consistently interconnected, producing meaningful data across the network without human interaction. Nowadays, the Healthcare system is widely interconnected with IoT environments to facilitate the best possible patient monitoring, efficient diagnosis, and timely operate with existing diseases towards the patients. Concerning the security and privacy of the patient's information. This paper is focused on Secure surveillance mechanism for a medical healthcare system with enabled internet of Things (sensors) by intelligently recorded video summary into the server and keyframes image encryption. This paper is twofold. Firstly, a well-organized keyframe extraction mechanism is called to extract meaningful image frames (detected normal/abnormal activities keyframe) by the visual sensor with an alert sent to the concerned authority in the healthcare system. Secondly, the final decision about the happened activity with extracted keyframes to keep highly secure from any adversary, and we propose an efficient probabilistic and lightweight encryption algorithm. Our proposed mechanism verifies effectiveness through producing results, robustness in nature, minimum execution time, and comparatively secure than other images (keyframes) encryption algorithms. Additionally, this mechanism can reduce storage, bandwidth, required transmission cost, and timely analysis of happened activity from any adversary with protecting the privacy of the patient's information in the IoT enabled healthcare system.

The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine learning techniques. The system is developed based on classification algorithms includes Support vector machine, Logistic regression, Artificial neural network, K-nearest neighbor, Naïve bays, and Decision tree while standard features selection algorithms have been used such as Relief, Minimal redundancy maximal relevance, Least absolute shrinkage selection operator and Local learning for removing irrelevant and redundant features. We also proposed novel fast conditional mutual information feature selection algorithm to solve feature selection problem. The features selection algorithms are used for features selection to increase the classification accuracy and reduce the execution time of classification system. Furthermore, the leave one subject out cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The performance measuring metrics are used for assessment of the performances of the classifiers. The performances of the classifiers have been checked on the selected features as selected by features selection algorithms. The experimental results show that the proposed feature selection algorithm (FCMIM) is feasible with classifier support vector machine for designing a high-level intelligent system to identify heart disease. The suggested diagnosis system (FCMIM-SVM) achieved good accuracy as compared to previously proposed methods. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.

The tribal belt of the Hindu Kush mountains is famous for its unique culture, ethnography, wild food plants, food systems, and traditional knowledge. People in this region gather wild plants and plant parts using them directly or in traditional cuisine, or sell them in local markets. However, there is a huge lack of documentation of the food system, particularly that related to wild food plants (WFP). In the current study, we focus on the uses and contributions of WFPs in the traditional tribal food system of the Hindu Kush valleys along the Pakistan–Afghanistan border. Ethnobotanical data were gathered through questionnaire surveys of 84 informants, including 69 men and 15 women, belonging to 21 different villages of the chosen area. In tribal societies men and women rarely mix and thus very few women took part in the surveys. We documented 63 WFP species belonging to 34 botanical families, of which 27 were used as vegetables, 24 as fruits, six in different kinds of chutneys (starters), and six as fresh food species. Fruits were the most used part (41%), followed by leaves (24%), aerial parts (24%), seeds (7%), stems (3%), and young inflorescences (1%). The reported uses of Carthamus oxyacantha, Pinus roxburghii seeds, and Marsilea quadrifolia leaves are novel for the gastronomy of Pakistan. The results reveal that WFPs provide a significant contribution to local food systems and play a role in addressing human nutritional needs, which are usually not met through farming practices. The tribal peoples of the Hindu Kush use WFPs for their nutritional value, but also as a cultural practice—an inseparable component of the tribal community’s lifestyle. This important traditional knowledge about the gathering and consumption of WFPs, however, is eroding at an alarming rate among younger generations due to the introduction of fast-food, modernization, and globalization. Therefore, appropriate strategies are imperative not only to safeguard traditional plants and food knowledge and practices, as well as the cultural heritage attached to them, but also to foster food security and thus public healthcare via local wild foods in the region.

Background: Tuberculosis is one of the causes of chronic meningitis which may lead to hydrocephalus. Uncertainty and doubtdominate all aspects of tuberculous meningitis.Objectives: To determine the frequency of hydrocephalus in tuberculous meningitis patients.Material & Methods: This was observational descriptive study conducted at department of neurosurgery and neurology ladyreading hospital Peshawar from Jan,2016 to Dec,2018. In this study there were total 93 cases of TBM with 95% confidencelevel,2.2% margin of error according to WHO calculator. Diagnosis was based on clinical features, CSF analysis and CT/MRI brain.Tuberculous meningitis patients of age range 15 to 50 years of either gender were included in this study. All those patients havingSOL in brain and other causes of meningitis were excluded from this study.The symptoms were fever, headache, and weight lossand neck rigidity with or without fits. These cases then underwent CSF analysis under aseptic measures. The findings to label TBMon CSF were low glucose ( 100 mg/dl) and high WBC count with predominance oflymphocytes. The data was analyzed with the help of SPSS version 23.Effect modifiers were stratified and and post stratificationChi-Square test was applied with P-value < 0.05 as significant. The cases of TBM were divided according to standard BMRC scaleinto three stages.Results: In this study there were total 93 cases with mean age of 37.11± 08.67 years. There were 54 (58.06%) males and 39(41.94%) females. Maximum cases were seen in stage II of TBM which affected 65 (69.89%) cases. Hydrocephalus was seen in 61(65.59%) of the cases. Hydrocephalus was significantly high in male gender as compared to females where it affected 39 (72.22%)of cases. It was also more common in cases that had age group 15-29 years affecting 40 (67.79%) of cases.Conclusion: Hydrocephalus is a common complication of tuberculous meningitis and it is predominantly seen in in male patients,stage II and III of TBM.Keywords: TBM, Hydrocephalus, Meningitis

Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency Cepstral Coefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.

The widely spread of fake news has significantly impacted our life in politics and economics. To solve this problem, different researchers have proposed various machine learning and deep learning models. However, most of them detect fake news without desired accuracy. Therefore, we proposed a deep learning framework that classifies fake news from real ones with 99.82% accuracy. This BiLSTM model was trained and tested on a fact-checking dataset. Furthermore, we used different model evaluation metrics like precision, recall, Fl-meassure, execution time to prove the efficiency of our model.

A heart disease diagnosis method has been proposed for effective heart disease diagnosis. In the proposed method Machine Learning (ML) classifiers have been used for detection of heart disease. Chi square feature selection algorithm has been used for related feature selection to improve the prediction performance of machine learning models. Cross validation, method Hold out has been employed for model hyper parameters tuning and best model selection. Furthermore, performance evaluation metrics, such as classification accuracy, specificity, sensitivity, Matthews' correlation coefficient and execution time have been used for model performance evaluation. The Cleveland heart disease data set has been used for testing of the proposed method. The experimental results demonstrated that proposed method has achieved high performance as compared to state of the art methods. Furthermore, the proposed method performance has been compared with deep learning model. Thus, the proposed method will support the medical professional to diagnosis heart disease efficiently and could easily incorporated in healthcare for diagnosis of heart disease.

The breast cancer is a critical female disease and its proper identification is very essential for better cure and recovery. The diagnosis of BC is a critical issue for clinical specialists and scholars. Different researchers proposed breast cancer diagnosis methods using deep learning techniques. However, these proposed methods not diagnosis breast cancer accurately. In order to tackle the issue of accurate detection of breast cancer we proposed a 3-layers CNN architecture for accurate detection of breast cancer. The proposed model has been trained and tested on Breast histology images data set. The cross validation method Hold out has been applied for best model selection and hyper parameters tuning. Furthermore, different model evaluation metrics have been used for model performance evaluation. The experimental results demonstrated that propped method is more suitable for breast cancer and it would be incorporated in health care successfully.

The increasing demands and requirements of mobile communication are gradually rendering the numerous current waveform designs obsolete. For example, in the realization of 5G, several waveforms are proposed. These waveforms are promising, but not without their respective drawbacks. Considering the ever-increasing demands, these drawbacks might make them unsuitable in higher generations of mobile networks such as 6G. Wavelet-based waveforms are considerable alternatives in meeting the ever-growing demands of future mobile communication. In this work, we investigate a wavelet Orthogonal Frequency Division Multiplex (WOFDM) algorithm that automatically adjusts to the constellation order according to the channel state. This we refer to as Link Adaptive Wavelet OFDM (LA-WOFDM). With this, we achieve a spectral efficient waveform while maintaining a balance in link quality. A spectral efficient waveform system yields higher power efficiency. Finally, our results show that Link Adaptive Wavelet-OFDM can compete with the state of the art.

With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.

The patient of Parkinson's disease (PD) is facing a critical neurological disorder issue. Efficient and early prediction of people having PD is a key issue to improve patient's quality of life. The diagnosis of PD specifically in its initial stages is extremely complex and time-consuming. Thus, the accurate and efficient diagnosis of PD has been a significant challenge for medical experts and practitioners. In order to tackle this issue and to accurately diagnosis the patient of PD, we proposed a machine-learning-based prediction system. In the development of the proposed system, the support vector machine (SVM) was used as a predictive model for the prediction of PD. The L1-norm SVM of features selection was used for appropriate and highly related features selection for accurate target classification of PD and healthy people. The L1-norm SVM produced a new subset of features from the PD dataset based on a feature weight value. For the validation of the proposed system, the K-fold cross-validation method was used. In addition, the metrics of performance measures, such as accuracy, sensitivity, specificity, precision, F1 score, and execution time, were computed for model performance evaluation. The PD dataset was in this paper. The optimal accuracy achieved the best subset of the selected features that might be due to various contributions of the PD features. The experimental findings of this paper suggest that the proposed method can be used to accurately predict the PD and can be easily incorporated in healthcare for diagnosis purpose. Currently, the computer-based assisted predictive system is playing an important role to assist in PD recognition. In addition, the proposed approach fills in a gap on feature selection and classification using voice recordings data by properly matching the experimental design.

The spam detection is a big issue in mobile message communication due to which mobile message communication is insecure. In order to tackle this problem, an accurate and precise method is needed to detect the spam in mobile message communication. We proposed the applications of the machine learning-based spam detection method for accurate detection. In this technique, machine learning classifiers such as Logistic regression (LR), K-nearest neighbor (K-NN), and decision tree (DT) are used for classification of ham and spam messages in mobile device communication. The SMS spam collection data set is used for testing the method. The dataset is split into two categories for training and testing the research. The results of the experiments demonstrated that the classification performance of LR is high as compared with K-NN and DT, and the LR achieved a high accuracy of 99%. Additionally, the proposed method performance is good as compared with the existing state-of-the-art methods. Ministry of Education of the People's Republic of China [DCA190332].

The heart of the current wireless communication systems (including 5G) is the Fourier transform-based orthogonal frequency division multiplex (OFDM). Over time, a lot of research has proposed the wavelet transform-based OFDM as a better replacement of Fourier in the physical layer solutions because of its performance and ability to support network-intensive applications such as the Internet of Things (IoT). In this paper, we weigh the wavelet transform performances against the future wireless application system requirements and propose guidelines and approaches for wavelet applications in 5G waveform design. This is followed by a detailed impact on healthcare. Using an image as the test data, a comprehensive performance comparison between Fourier transform and various wavelet transforms has been done considering the following 5G key performance indicators (KPIs): energy efficiency, modulation and demodulation complexity, reliability, latency, spectral efficiency, effect of transmission/reception under asynchronous transmission, and robustness to time-/frequency-selective channels. Finally, the guidelines for wavelet transform use are presented. The guidelines are sufficient to serve as approaches for tradeoffs and also as the guide for further developments.

In the field of object detection, recently, tremendous success is achieved, but still it is a very challenging task to detect and identify objects accurately with fast speed. Human beings can detect and recognize multiple objects in images or videos with ease regardless of the object’s appearance, but for computers it is challenging to identify and distinguish between things. In this paper, a modified YOLOv1 based neural network is proposed for object detection. The new neural network model has been improved in the following ways. Firstly, modification is made to the loss function of the YOLOv1 network. The improved model replaces the margin style with proportion style. Compared to the old loss function, the new is more flexible and more reasonable in optimizing the network error. Secondly, a spatial pyramid pooling layer is added; thirdly, an inception model with a convolution kernel of 1 ∗ 1 is added, which reduced the number of weight parameters of the layers. Extensive experiments on Pascal VOC datasets 2007/2012 showed that the proposed method achieved better performance.

The most common use of robots is to effectively decrease the human’s effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods.

Asthma is an inflammatory and airway-induced lung disease that causes breathlessness, wheezing and often life-threatening attacks. It is necessary for patients with asthma to predict severe exacerbation caused by uncontrolled asthma. So, there is a need for a framework that can predict the likelihood of asthma in a patient with maximum accuracy. In this paper, an effective mechanism for asthma disease prediction has been used by mining the data containing patients’ previous health records. Four machine learning classification algorithms named as Naive Bayes, J48, Random Forest and Random tree are used in these experiments to predict asthma disease at an early stage. The performance of all four algorithms is evaluated on various measures. Accuracy is measured over instances that are classified correctly and incorrectly. After experiments, results show that Naive Bayes approaches 98% with the highest accuracy.

Poliomyelitis is a disease which is spread through virus called enter virus. Its treatment is difficult but can be trim down through prevention strategies like on proper vaccination and management. For this purpose community awareness and mobilization is much needed factor, This disease is eradicated by the develop countries but still present in under develop countries due to the poor hygiene life style and lack of proper awareness about polio disease. Currently, Pakistan, Nigeria and Afghanistan are facing huge challenges to eradicate polio. It mostly affect children under age five and cause paraplegics condition to some part of the body, to cope with this problem a feature model has been proposed to resolve most of the network management issues required to communicate among the polio eradication health centers, like collecting the exact number of affected, missing and refusal children, launching a campaign in a proper hierarchical approach.

Due to the exceptional growth of the electronic industry and the cause of technological advancement which is generated an exceptionally high rate of electronic waste in India particularly the Delhi national capital region. This research is focused on E-waste generation and awareness of its managing disposal practices based on a high standard questionnaire. This study focusses only on mobile, laptop, notebook, computer and Television as an E-waste. The structured questionnaire is widely spread and received around 200 households of the different working sectors as well as different income groups individual’s reactions. The findings of individual awareness of generation and managing disposal practices are considerably lacking. It should be negative impact of health issues in the Delhi (NCR) area.

The diagnosis of heart diseases through invoice based techniques as well as ordinary medical based methods are not reliable. On other hand, non-invoice based techniques are more effective for heart disease diagnosis. Therefore, we check the capability of various Machine Learning (ML) classifiers and deep learning classifier for heart disease identification in this paper. Six machine-learning classifiers and BPNN were used in order to check which one classifier is more effective for diagnosis the heart disease. The feature selection algorithm Relief was used for selection of important features and on these selected features, classifiers performances were also computed. Ensemble machine learning techniques (boosting, bagging, stacking) were used to further increase the classifiers performance. Furthermore, cross-validation techniques k-folds was also used. Additionally backward propagation neural network (BPNN) was also used for classification purpose because deep learning algorithm not need feature selection algorithms and it automatically select important features for good result. Based on model performance evaluation metrics the SVM (RBF) performed excellently on full features achieved accuracy 86%, and 88% accuracy on selected features as compared other classifiers. Through Ensemble learning techniques, SVM obtained the classification accuracy 92.30%. The BPNN achieved 93% classification accuracy. Thus the performance of deep neural networks learning algorithm is better than traditional machine learning algorithms. As per our experimental results shows that the performance of BPNN based diagnosis system is more effective for heart disease diagnosis

The security and privacy of medical data are highly required when it involved in connecting with smart and intelligent sensor devices in the hospital ecosystem. The Internet of things is mutually integrated with healthcare to produce effective and smart working system but it still needs better security mechanism which can protect patient’s privacy. This paper is focusing to protect medical data by using image encryption techniques. In which we are applying three rounds of high-speed scrambling as well as pixel adaptive diffusion concept for shuffling random neighboring pixels. In addition, implementing pixel adaptive diffusion our mechanism is used modulo arithmetic operation. Simulations and experiments demonstrate that the encryption mechanism employing modulo arithmetic operation have comparatively higher levels of security. It can adapt better than several traditional as well as state-of - the-art image encryption.

The Alzheimer’s disease accurate early-stage detection is critically necessary for effective treatment and recovery. Therefore, accurate detection of Alzheimer’s disease is a great research problem. Different researchers used various techniques to detect Alzheimer’s disease effectively however; these methods still have lack of prediction accuracy. In this study we proposed machine learning-based method to diagnosis Alzheimer’s disease accurately. We used machine learning classifiers for accurate prediction of Alzheimer’s disease. Alzheimer’s Disease Neuroimaging Initiative data set has been used to check the proposed method performance. The experimental results demonstrate that logistic regression performance was excellent in terms of accuracy and achieved optimal accuracy 98.12%.Thus, it is recommended for effective early detection of Alzheimer’s Disease.

Detection of Heart Disease (HD) by using models of machine learning (ML) is very effective in early stages. The HD treatment and recovery is effective if detected the disease at initial stages. HD identification by machine learning (ML) techniques has been developed to assist the physicians. In this study we proposed an Identification system by using ML models to classify the HD and healthy subjects. Sequential backward selection of feature algorithm was used to select more appropriate features to increase the classification accuracy and reduced the computational time of predictive system. Cleveland heart disease dataset was for evaluation of the system. The dataset 70% used for training and remaining for validation. The proposed system performances have been measured by using evaluation metrics. The experimental results shows that Sequential Backward Selection (SBS) algorithms choose appropriate features and these features increase the accuracy using K-Nearest Neighbor supervised machine learning classifier. The good accuracy of this study suggests that the proposed model will effectively identify the HD and healthy subjects.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient&rsquo, s clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

The accurate and efficient diagnosis of breast cancer is extremely necessary for recovery and treatment in early stages in IoT healthcare environment. Internet of Things has witnessed the transition in life for the last few years which provides a way to analyze both the real-time data and past data by the emerging role of artificial intelligence and data mining techniques. The current state-of-the-art method does not effectively diagnose the breast cancer in the early stages, and most of the ladies suffered from this dangerous disease. Thus, the early detection of breast cancer significantly poses a great challenge for medical experts and researchers. To solve the problem of early-stage detection of breast cancer, we proposed machine learning-based diagnostic system which effectively classifies the malignant and benign people in the environment of IoT. In the development of our proposed system, a machine learning classifier support vector machine is used to classify the malignant and benign people. To improve the classification performance of the classification system, we used a recursive feature selection algorithm to select more suitable features from the breast cancer dataset. The training/testing splits method is applied for training and testing of the classifier for the best predictive model. Additionally, the classifier performance has been checked on by using performance evaluation metrics such as classification, specificity, sensitivity, Matthews’s correlation coefficient, F1-score, and execution time. To test the proposed method, the dataset “Wisconsin Diagnostic Breast Cancer” has been used in this research study. The experimental results demonstrate that the recursive feature selection algorithm selects the best subset of features, and the classifier SVM achieved optimal classification performance on this best subset of features. The SVM kernel linear achieved high classification accuracy (99%), specificity (99%), and sensitivity (98%), and the Matthews’s correlation coefficient is 99%. From these experimental results, we concluded that the proposed system performance is excellent due to the selection of more appropriate features that are selected by the recursive feature selection algorithm. Furthermore, we suggest this proposed system for effective and efficient early stages diagnosis of breast cancer. Thus, through this system, the recovery and treatment will be more effective for breast cancer. Lastly, the implementation of the proposed system is very reliable in all aspects of IoT healthcare for breast cancer.

Internet of Things (loT)is the fast-growing emerging technology and it is making our daily life (activities)easier and better. The loT is connecting millions of sensors and heterogeneous devices having different computation, communication, and storage capabilities which are increasing rapidly. The loT network is dissimilar and larger from the conventional network. However, different natures of the connected devices raising security concerns and challenges which affect the importance of loT technology. In this paper, we proposed an authentication scheme based on the OAuth 2.0 protocol to secure access loT network by providing authentication service. An OAuth 2.0 protocol is used to propose authentication mechanism to allow only authorized and authentic users by comparing user information and access tokens in the security manager local database and denies the access to the loT network. It also keeps safe loT network from different types of attacks like impersonation and replays attacks etc.

The optoelectronic industry is indebted a lot to the phenomenon of light-matter coupling. The light coupled with a semiconductor material generates the quasi-particles which are named as polaritons. The features of polaritons being lighter in weight and faster in speed pave the way for the optoelectronic devices to be ultrafast lighter and less noisy. The characteristics of 2D materials to be selectively controlled by polarization are also supportive for the optical devices to be controlled by polarized light. This study presents the idea of controlling ON-OFF method of an optical switch by making use of Optical Stark Effect in semiconductor material of WS2. This work is proposing to control the gate signal triggered by the OSE in two different polarizations i.e., Right Circular Polarization and Left Circular Polarization. Moreover, the most important optical bistability and hysteresis curves of polaritons stability in transition metals dichalcogenides semiconductor are also analyzed dynamically.

The accurate diagnosis of Parkinson disease specifically in its initial stages is extremely complex and time consuming. Thus the accurate and efficient diagnosis of Parkinson disease has been a significant challenge for medical experts and researchers. In order to tackle the accurate diagnosis of Parkinson disease issue we proposed machine learning and deep neural networks based non-invasive prediction system for accurately and on time diagnosis of Parkinson disease. In the development of the system machine learning predictive models such as support vector machine, logistic regression and deep neural network were used for people with Parkinson disease and healthy people classification. The data set was splits into 70% for training purpose and 30% for testing. Furthermore, performance evaluation metrics such as classification accuracy, sensitivity, specificity and Matthews's correlation coefficient were utilized for model performance evaluation. The Parkinson disease dataset of 23 attributes and 195 instances available on UCI machine learning repository was used for testing of the proposed system. Through our experimental results analysis shows that the proposed system classified the Parkinson disease and healthy people effectively. We also investigated that deep neural performance of classification was excellent as compared to traditional machines learning classifiers. These finding suggest that the proposed diagnosis system could be used to accurately predict Parkinson disease.

Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.

Background To test the null hypothesis that there is no difference in the apical root resorption seen after orthodontic treatment with the conventional brackets and the self-ligating brackets. Methods Pre-treatment and post-treatment periapical radio-graphs of 70 patients, (35 treated with the Damon3 0.022” bracket and 35 with the 0.022” 3 M bracket) were studied. The long cone paralleling technique was used for all the radio-graphs. Any image distortion between the pre-treatment and post-treatment radio-graph was calculated and compensated for by using the crown length measurements, on the assumption that the crown length remains unaltered during the treatment period. Quantitative measurements of crown and root lengths for the maxillary and the mandibular central and lateral incisors were compared. Means and standard deviations for the percentage root resorption per tooth group were calculated. A paired t-test and non paired t-test analysis was performed to determine whether there was an appliance, treatment time, or initial age effect on the amount of root resorption seen after treatment. Result No statistically significant difference in root resorption between the two appliance systems was found. The patient’s degree of root resorption were graded as grade 1 and grade 2 in the self-ligating group which is more than the conventional group. Conclusions There was no significant difference in root resorption between self-ligating brackets and conventional brackets in severe crowding incisors subjects.

Lung cancer is one of the most prevalent malignancies in the world and both incidence and mortality rates are continuing to rise in Pakistan. However, epidemiological studies to identify common lung cancer determinants in the Pakistani population have been limited.In this retrospective case-control study, 400 cases and 800 controls were enrolled from different hospitals of all provinces of Pakistan. Information about socio-demographic, occupational, lifestyle and dietary variables was extracted by questionnaire from all subjects. Odd ratios (ORs) and 95% confidence intervals (CIs) were calculated. and dose-response associations were also assessed for suitable factors.Strong associations were observed for smoking (OR=9.4, 95%CI=6.9-12.8), pesticide exposure (OR=5.1, 95%CI=3.1-8.3), exposure to diesel exhaust (OR=3.1, 95%CI=2.1-4.5), red meat consumption (OR=2.9, 95%CI=1.8-4.7) and chicken consumption (OR=2.8, 95%CI=1.7-49). Other associated factors observed were welding fumes (OR=2.5, 95%CI=1.0-6.5), sedentary living (OR=2.0, 95%CI=1.6-2.6), family history (OR=2.0, 95%CI=0.8-4.9), wood dust (OR=1.9, 95%CI=1.2- 3.1), tea consumption (OR=1.8, 95%CI=1.2-2.6), coffee consumption (OR=1.8, 95%CI=1.1-2.8), alcoholism (OR=1.7, 95%CI=1.1-2.5) and asbestos exposure(OR=1.5, 95%CI=0.5-4.4). Consumption of vegetables (OR=0.3, 95%CI=0.2-0.4), juices (OR=0.3, 95%CI=0.3-0.4), fruits (OR=0.7, 95%CI=0.5-0.9) and milk (OR=0.6, 95%CI=0.5- 0.8) showed reduction in risk of lung cancer. Strongest dose-response relationships were observed for smoking (?2=333.8, p≤0.0000001), pesticide exposure (?2=50.9, p≤0.0000001) and exposure to diesel exhaust (?2=51.8, p≤0.0000001).Smoking, pesticide exposure, diesel exhaust and meat consumption are main lung cancer determinants in Pakistan. Consuming vegetables, fruits, milk and juices can reduce the risk of lung cancer risk, as in other countries.

Background: Chemical pleurodesis is used for the management of malignant /recurrent pleural effusion and pneumothorax.Malignant pleural effusion is diagnosed on histopathology or malignant cells in pleural fluid. Pneumothorax is diagnosed by chestradiograph. Pleurodesis can be performed surgically or by using medical agents. Various pleurodesis agents have been used e.g.Tetracycline.Bleomycin. Talc and lodopovidone with different success and complications rates.Objective: This study was conducted to know the efficacy and safety of lodopovidone as pleurodesis agent in themanagement of pleural effusion (malignant/recurrent) and recurrent pneumothorax.Material and Methods: This was a descriptive study conducted at department of Pulmonology Lady Reading HospitalPeshawar from February 2018 to May 2019.Results:A Total of 213 patients were included in the study, among which 120 (56.3%) were males and 93 (43.6%) werefemales. Age range was from 20 to 83 years. One hundred and thirty eight patients (74.8%) had malignant pleural effusion and75 (35.2%) had recurrent pneumothorax. The success rate with lodopovidone pleurodesis was 85 % (181) as a whole with afailure rate of 13 %( 28) in pleural effusion and 2 %( 4) in pneumothorax. Chest pain (65.7%) was the most common side effectfollowed by fever (24.4%) and dyspnea (12.2%).No serious side effects were observed in this study.Conclusion: lodopovidone is easily available, safer, cheaper and effective agent for chemical pleurodesis in pneumothorax,malignant/recurrent pleural effusion.Keywords: Pleurodesis, lodopovidone. Malignant, Pleural Effusion, Pneumothorax.

Background The purpose of this systematic review is to identify and review the orthodontic literature with regards to assessing possible differences in canine retraction rate and the amount of antero-posterior anchorage (AP) loss during maxillary canine retraction, using conventional brackets (CBs) and self-ligating brackets (SLBs). Methods An electronic search without time or language restrictions was undertake in September 2014 in the following electronic databases: The Cochrane Oral Health Group’s Trials Register, CENTRAL, MEDLINE via OVID, EMBASE via OVID, Web of science. We also searched the reference lists of relevant articles. Quality assessment of the included articles was performed. Two of the authors were responsible for study selection, validity assessment and data extraction. Results Six studies met the inclusion criteria, including 2 randomized controlled trials and 4 control clinical studies. One was assessed as being at low risk of bias. Five trials were assessed as being at moderate risk of bias. The meta-analysis from 6 eligible studies showed that no statistically significant difference was observed between the 2 groups in the rate of canine retraction and loss of antero-posterior anchorage of the molars. Conclusion There is some evidence from this review that both brackets showed the same rate of canine retraction and loss of antero-posterior anchorage of the molars. The results of the present systematic review should be viewed with caution due to the presence of uncontrolled interpreted factors in the included studies. Further well-designed and conducted randomized controlled trials are required, to facilitate comparisons of the results.