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During the past years, multi-view clustering algorithms have demonstrated satisfactory clustering results by fusing the multiple views of the dataset. Nowadays, the researches of dimensionality reduction and learning discriminative features from multi-view data have soared in the literatures. As for clustering, generating the suitable subspace of the high dimensional multi-view data is crucial to boost the clustering performance. In addition, the relationship between the original data and the clusters still remains uncovered. In this article, we design a new multi-view low rank sparse representation method based on three-way clustering to tackle these challenges, which derive the common consensus low dimensional representation from the multi-view data and further proceed to get the relationship between the data items and clusters. Specifically, we accomplish this goal by taking advantage of the low-rank and the sparse factor on the data representation matrix. The $$L_{2,1}$$ norm is imposed on error matrix to reduce the impact of noise contained in the data. Finally, a new objective function is constructed to preserve the consistency between the views by using the low-rank sparse representation technique. The weighted low-rank matrix is utilized to build the consensus low rank matrix. Then, the whole objective function is optimized by using the Augmented Lagrange’s Multiplier algorithm. Further, to find the uncertain relationship between the data items and the clusters, we pursue the neighborhood based three-way clustering technique to reflect the data items into core and fringe regions. Experiments conducted on the real-world datasets show the superior performance of the proposed method compared with the state-of-the-art algorithms.

Clustering large and high-dimensional document data has got a great interest. However, current clustering algorithms lack efficient representation learning. Implementing deep learning techniques in document clustering can strengthen the learning processes. In this work, we simultaneously disentangle the problem of learned representation by preserving important information from the initial data while pushing the original samples and their augmentations together in one hand. Furthermore, we handle the cluster locality preservation issue by pushing neighboring data points together. To that end, we first introduce Contractive Autoencoders. Then we propose a deep embedding clustering framework based on contractive autoencoder (DECCA) to learn document representations. Furthermore, to grasp relevant document or word features, we append the Frobenius norm as penalty term to the conventional autoencoder framework, which helps the autoencoder to perform better. In this way, the contractive autoencoders apprehend the local manifold structure of the input data and compete with the representations learned by existing methods. Finally, we confirm the supremacy of our proposed algorithm over the state-of-the-art results on six real-world images and text datasets.

Numerous works implemented multi-view clustering algorithms in document clustering. A challenging problem in document clustering is the similarity metric. Existing multi-view document clustering methods broadly utilized two measurements: the Cosine similarity (CS) and the Euclidean distance (ED). The first did not consider the magnitude difference (MD) between the two vectors. The second can’t register the divergence of two vectors that offer a similar ED. In this paper, we originally created five models of similarity metric. This methodology foils the downside of the CS and ED similarity metrics by figuring the divergence between documents with the same ED while thinking about their sizes. Furthermore, we proposed our multi-view document clustering plan which dependent on the proposed similarity metric. Firstly, CS, ED, triangle’s area similarity and sector’s area similarity metric, and our five similarity metrics have been applied to every view of a dataset to generate a corresponding similarity matrix. Afterward, we ran clustering algorithms on these similarity matrices to evaluate the performance of single view. Later, we aggregated these similarity matrices to obtain a unified similarity matrix and apply spectral clustering algorithm on it to generate the final clusters. The experimental results show that the proposed similarity functions can gauge the similitude between documents more accurately than the existing metrics, and the proposed clustering scheme surpasses considerably up-to-date algorithms.

Nowadays, non-negative matrix factorization (NMF) based cluster analysis for multi-view data shows impressive behavior in machine learning. Usually, multi-view data have complementary information from various views. The main concern behind the NMF is how to factorize the data to achieve a significant clustering solution from these complementary views. However, NMF does not focus to conserve the geometrical structures of the data space. In this article, we intensify on the above issue and evolve a new NMF clustering method with manifold regularization for multi-view data. The manifold regularization factor is exploited to retain the locally geometrical structure of the data space and gives extensively common clustering solution from multiple views. The weight control term is adopted to handle the distribution of each view weight. An iterative optimization strategy depended on multiplicative update rule is applied on the objective function to achieve optimization. Experimental analysis on the real-world datasets are exhibited that the proposed approach achieves better clustering performance than some state-of-the-art algorithms.

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.

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.

The technological progression is raised as a hybrid ecosystem with the industrial Internet of Things (IIoT). Among them, healthcare is also broadly unified with the Internet of Things to develop an industrial forthcoming system. Utilizing this type of system can be facilitating optimum patient monitoring, competent diagnosis, intensive care, and including the appropriate operation against the existing critical diseases. Due to enormous data theft or privacy leakage, security, and privacy towards patient-based informative data, the preservation of personal patients’ informative data has now become a necessity in the digitized community. The produced article is underlined on handsomely monitoring, perceptively extracted keyframe, and further processed lightweight cosine functions using hybrid way chaotic map keyframe image encryption. Initially, a regular concept of extracted keyframe is deployed to salvage meaningful detected frames by transmitting an alert autonomously to the administration. Then, lightweight cosine function for encryption is employed. This encryption incorporates keyframe exceedingly secure and safe from the outside world or any adversary. Our proposed methodology validates effectiveness throughout the IIoT ecosystem. The produced outcome is highly acceptable and has minimum execution time, robustness, and reasonably adopted cost-effective, secure parameter than any other (keyframes) image encryption methods. Furthermore, this methodology has optimally reduced bandwidth, essential communicating price, transmission cost, storage, and immediately judicious analysis of each occurred activity from the outside world or any adversary to remain secure and confident about the real patient-based data in the smartly developed environment.

Brain cancer is a life-threatening disease and hampers the normal operation of the human body. For correct diagnosis and methodical treatment planning, it is essential to detect the brain tumor in initial stages of development. This study proposes an intelligent diagnostic method for early detection of brain tumor. In the developed method, the deep features of magnetic resonance imaging (MRI) scans are used as the input of support vector machine (SVM). In the first step of the proposed method, Grab cut method is applied for segmenting tumor region, then the segmented images are fed to convolutional neural network (CNN) for deep feature extraction. Following feature extraction module, minimum Redundancy Maximum Relevance (mRMR) algorithm is used to select the most efficient deep features. Finally, the selected deep features are fed into SVM in the classification module. In addition, we applied the black widow optimization algorithm (BWOA) for optimal tuning of hyperparameters of CNN and SVM. The developed method applied on BraTS 2015 datasets and the obtained results showed that the developed method is effective and may be employed in computer-aided diagnosis systems to classify the type of tumor.

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.

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.

Many works implemented multi-view clustering algorithms in document clustering. One challenging problem in document clustering is the similarity metric. Existing multi-view document clustering methods widely used two measurements: the Cosine similarity and the Euclidean Distance (ED). The first did not consider the magnitude between the two vectors. The second cannot compute the dissimilarity of two vectors that share the same ED. In this paper, we proposed a multi-view document clustering scheme to overcome these drawbacks by calculating the heterogeneity between documents with the same ED while taking into consideration their magnitudes. The experimental results show that the proposed similarity function can measure the similarity between documents more accurately than the existing metrics, and the proposed document clustering scheme goes beyond the limit of several state-of-the-art algorithms.

In recent years, datasets which exist in present world are comprising of various representations of the data or in multiview environment, which frequently give the important data to each other. Multi-view clustering based on Non-negative matrix factorization (NMF) has turned to be a very hot direction of research in the field of Pattern Reognition, Machine Learning (ML), and data mining. and data mining due to unsupervised confuse information of Numerous Views. The main problem of employing NMF to multi-view clustering is how to define the factorizations to give significant and commensurate clustering solutions. Specially, multi-view clustering based NMF has achieved extensive attention due to its dimensionality reduction property. Existing methods based on NMF barely produced meaningful clustering solution from heterogeneous numerous views due to their complementary behaviors. To address this issue, we design a innovative NMF technique based Multiview clustering approach, which gives the more meaningful and compatible clustering solution over Numerous Views. The main outcome of the work, is to a design combined NMF method with view weight and constraint co-efficient which will bring the clustering solution to a common point for each view. The effectiveness of propose method is validated by conducting the experiments on real-world datasets.

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.

Surfactant-free silver nanoparticles (NPs) were synthesized by ablating a silver target placed in pure water by nanosecond pulsed Nd:YAG laser beam. UV–Vis spectroscopy and scanning electron microscopy (SEM) were employed to characterize the NPs. The diagnostics revealed that the NPs concentration in liquid linearly increases with the laser pulse energy up to 40 mJ and then starts to decrease with further increase of the laser energy. The diameter of NPs ranges from approximately 20 to 100 nm, with the average diameter of 50 nm. The average diameter of NPs does not seem to vary with the laser pulse energy. The surfactant-free Ag NPs were used to degrade methylene blue (MB) and methyl orange (MO) dyes. The surfactant-free Ag NPs showed high catalytic activity and completely degraded MB and MO. Furthermore, the SERS study of Rhodamine 6G using Ag NPs as a substrate resulted in significant Raman enhancement. In order to demonstrate that the surfactant-free nature of the Ag NPs is responsible for their better catalytic ability, a cationic surfactant is attached to the Ag NPs, which resulted in substantial reduction of its catalytic ability. These results confirm that the better catalytic ability and Surface Enhanced Raman Spectroscopy (SERS) signal enhancement is primarily due to the surfactant-free nature of the Ag NPs synthesized by pulse laser ablation.

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.

To assess early and late outcome in severe chronic kidney disease patients undergoing revascularisation.The retrospective ambi-directional cohort study was conducted at Tabba Heart Institute, Karachi, and comprised data from May, 2012, to July,2016, related to severe chronic kidney disease patients with creatinine clearance30ml/min or end-stage renal disease on haemodialysis who had undergone coronary artery bypass graft / percutaneous coronary intervention. Early outcome was in-hospital major adverse cardiac event, like mortality, stroke and new haemodialysis. Late outcome was major adverse cardiac event, like mortality, stroke, re infarction and re-revascularisation. Data was analysed using Stata 12.1.Of the 228 patients with mean age of 64.2±10.8 years, 109(47.8%) with a maen age of 65.4±11.6 had undergone percutaneous coronary intervention, and 119(52.2%) with a mean age of 64.2±10.8 years had undergone coronary artery bypass graft. Overall mortality was 36(15.8%) patients; 15(13.7%) percutaneous coronary intervention, 21(17.6%)coronary artery bypass graft (p0.05). Predictors of in-hospital adverse events were coronary artery bypass graft and cardiogenic shock (p0.05). Follow-up was available in 181(94.3%) patients with a mean duration of 22.0±13.9 months. Heart failure and post-procedure stroke were independent predictors of late outcome (p0.05).Among patients with severe chronic kidney disease or end stage renal disease undergoing revascularisation, percutaneous coronary intervention PCI was performed in patients with less complex anatomy or as emergency for acute ST-elevation myocardial infarction. Coronary artery bypass graft patients had higher early mortality, but improved late survival.