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The current wave of technologies with increased awareness among customers and retaining customers has a vital role in the growth of the company. A good indicator of service satisfaction of customers and service quality is customer churn. In order to enable the organizations to understand customers for churning, intelligible and accurate models are needed. There have been several techniques of data mining that were applied for the prediction of churn. The extensive research in Artificial Intelligence has made it feasible to study and learn the aspects accounting for such customer churn. The work presents effective solutions to all these challenging problems in Customer Churn Prediction (CCP). The study uses datasets in the telecommunication industry, the Artificial Neural Networks (ANN), and the Random Forests (RF) to determine the factors that influence consumer churn. A hybrid ANN-based work is proposed for predicting CCP. The results of the experiment proved that the proposed method achieves better levels of performance. The classification accuracy of ANN-4 hidden layers improves its result compared to RF and ANN-2 hidden layers. The maximum accuracy attained by ANN-2 hidden layers is 88.14% and by ANN-4 hidden layers is 90.34%.

Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to extract the exact walking features of individuals. Walking features depends on biological as well as the physical feature of the object; hence, it is unique to every individual. In this work, gait features are used to identify an individual. The steps involve object detection, background subtraction, silhouettes extraction, skeletonization, and training 3D Convolution Neural Network (3D-CNN) on these gait features. The model is trained and evaluated on the dataset acquired by CASIA—B Gait, which consists of 15,000 videos of 124 subjects’ walking pattern captured from 11 different angles carrying objects such as bag and coat. The proposed method focuses more on the lower body part to extract features such as the angle between knee and thighs, hip angle, angle of contact, and many other features. The experimental results are compared with amongst accuracies of silhouettes as datasets for training and skeletonized image as training data. The results show that extracting the information from skeletonized data yields improved accuracy.

With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. This study shows the systematic review of promising research areas and applications of DL models in medical diagnosis and medical healthcare systems. The prevalent DL models, their architectures, and related pros, cons are discussed to clarify their prospects. Many deep learning networks have been useful in the field of medical image processing for prognosis and diagnosis of life-threatening ailments (e.g., breast cancer, lung cancer, and brain tumor, etc.), which stand as an error-prone and tedious task for doctors and specialists when performed manually. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement.

Data security in centralized storage needs advancement in privacy standards because of all of the cloud. The data security in the cloud has been well provided by security industries like cloud network security, data center security, distributed security and soon, also their exist numerous techniques to preserve the privacy of cloud users. The earlier methods enforce user privacy by restricting the malicious access from various users. Data authentication is provable access to keep privacy among other standards. However, the privacy of cloud users has been breached on several occasions. To improve cloud security and enforce efficient privacy preservation, a multi-level micro access restriction algorithm has been presented in this paper. The cloud data has been indexed in multiple levels, the data present in each level has been restricted using the profile and set if encryption standards. The user request has been evaluated for its trusted access according to the access grant present in profile data. Similarly, the cloud data has been encrypted with the user key and the key belongs to the data owner. The method estimates micro access trust weight (MATW), which has been used to restrict the user from malicious access and to preserve user privacy. The method improves the performance of cloud security and introduces higher privacy preservation accuracy.

Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication of secret images in the context. Stego images thus generated are subjected to a deep learning approach to assess if it can be classified as a cover or not. If not, the HS parameter is modified to generate stego images in such a way to classify it as a cover image. Thus it is difficult to suspect the existence of a secret image by the Human Visual System (HVS). The efficiency of our method is analyzed by comparing it with related methods present in the literature. Average NCC values between the original secret image and the extracted secret image are 0.8917 which is higher than the schemes in the literature. Average PSNR values of the stego image are 36.375 even when the embedding rate is increased to 4 bits per pixel. The analysis was done on security and robustness also reveals better results. From the experimental analysis, it is proved that the proposed method is superior to the related methods of the literature.

Cloud computing is widely accepted by both individuals and enterprises alike for the storage of multimedia contents. It is due to the introduction of a new architecture where the cost of computation, storage, and services needed for maintenance for storage of multimedia are less. Cloud computing addresses the scarcity of resources for clients by offering options to pay for services only as they are used. But once the organization’s multimedia contents are uploaded into cloud space, the user loses control over their contents which may no longer be safe. The cloud user has to take some measure to avoid privacy issues. Steganography is preferred over encryption for providing multimedia security as content concealed in a cover image is not revealed. The multimedia content is transformed using Discrete Rajan Transform (DRT) and embedded into a chosen cover image which is created by Integer Wavelet using Diamond Encoding Scheme. Generated stego images are stored in the cloud. When the multimedia content is required, stego images are downloaded from the cloud and are subjected to inverse transform of IWT. SVM provides Good learning ability to our extraction process which makes our algorithm more robust to various attacks, viz., salt and pepper noise, Gaussian noise, cropping, compression, etc. Experimental values for Peak Signal to Noise Ratio (PSNR) for two secret images are 53 and 50 respectively which is better over the available schemes in the literature. Similarly for robustness and security evaluation, our scheme provides a better result.

The approximate controllability of second-order integro-differential evolution control systems using resolvent operators is the focus of this work. We analyze approximate controllability outcomes by referring to fractional theories, resolvent operators, semigroup theory, Gronwall’s inequality, and Lipschitz condition. The article avoids the use of well-known fixed point theorem approaches. We have also included one example of theoretical consequences that has been validated.

In this manuscript, we deal with the nonlocal controllability results for the fractional evolution system of $1< r 1 < r < 2 in a Banach space. The main results of this article are tested by using fractional calculations, the measure of noncompactness, cosine families, Mainardi’s Wright-type function, and fixed point techniques. First, we investigate the controllability results of a mild solution for the fractional evolution system with nonlocal conditions using the Mönch fixed point theorem. Furthermore, we develop the nonlocal controllability results for fractional integrodifferential evolution system by applying the Banach fixed point theorem. Finally, an application is presented for drawing the theory of the main results.

In our manuscript, we extend the controllability outcomes given by Bashirov (Math. Methods Appl. Sci. 44(9):7455–7462, 2021) for a family of second-order semilinear control system by formulating a sequence of piecewise controls. This approach does not involve large estimations which are required to apply fixed point theorems. Therefore, we avoid the use of fixed point theory and the contraction mapping principle. We establish that a second-order semilinear system drives any starting position to the required final position from the domain of the system. To achieve the required results, we suppose that the linear system is exactly controllable at every non-initial time period, the norm of the inverse of the controllability Grammian operator increases as the time approaches zero with the slower rate in comparison to the reciprocal of the square function, and the nonlinear term is bounded. Finally, an example has been presented to validate the results.

Due to increased attraction in cloud computing, mobile devices could store or acquire private and confidential information from everywhere at any point in time. In parallel, the information safety issues over mobile computing become rigorous and retard increased advancements in the mobile cloud. Crucial analysis were performed to enhance the safety in cloud computing. Most of them are not appropriate for mobile cloud computing due to limited energy resource, thus mobile devices are unable to perform assessments and complex tasks. The crucial requirement of mobile cloud application is to provide solution with minimum computational overhead. Thus the aim of the research is to design a trivial information relaying scheme (TIRS) for mobile cloud computing. The proposed scheme implements Ciphertext Policy Attribute-based Encryption (CP-ABE) to alter the general framework of access governance hierarchy to make it appropriate for mobile cloud environment. The TIRS displaces immense segments of the assessment concentrated access governance hierarchy modifications in CP-ABE from smart devices to the peripheral proxy servers. Furthermore, TIRS initiates element portrayal field to plan indolent cancellation which is a thriving dispute for CP-ABE system. The experimental analysis depicts that TIRS successfully minimize the overheads during user relaying information over the mobile cloud environment.

Ant colony optimization, a swarm intelligence technique, inspired by the foraging behavior of ants in colonies was used in the past research works to compute the optimal path. The existing works of routing using ant colony optimization of MANETS face challenges in load balancing and energy efficiency. The proposed A-EEBLR approach chooses the next hop node based on metrics like delay, energy drain rate, congestion, link quality. Based on these metrics the probability of choosing next hop node as neighbor node is determined. The next hop probability determines the forward and backward ant agents to establish multiple paths among which the most optimal path is selected for transmission. The implementation results shows that the proposed A-EEBLR approach outperforms the existing A-ESR approach when evaluated by varying the number of packets, number of nodes and node mobility.

Environmental warning is caused by IT industry which critically leads the global pollution with huge amount of toxic carbon emission which is drastically increasing day by day due to demand and usage raised. Due to the environmental warning the industries are in awful need of reducing the carbon foot print by inducing green computing. This paper has achieved green computing by implementing two different algorithms (1) Water Shower Model (WSM) and (2) Trigger—WSM load balancing algorithms, and two different techniques (3) Recommending Containers over virtual machine techniques and 4. DVFS (Dynamic Voltage Frequency Scaling) modeling. The observation between the recommendation systems for container over virtual machine for a sample of four containers with one application each and four virtual machine with one application each is monitored for carbon emission equivalent in kg co2 for about 1 week is 14 kg co2 for container and 84.4 kg co2 for virtual machine, where a drastic difference in the amount of carbon emission is seen. So recommending container will be the best possible solution for the IT based on applications, by enforcing these ideas and techniques the carbon emission can be drastically decreased and the amount of carbon footprint in the atmosphere will also be reduced. The amount of power consumption utilized for the same model is 15.71367 W for container and 94.72667 W for virtual machine is also observed; in the field of IT the power consumption also to be taken into consideration for reducing carbon emission. The recommendation system along with the proposed algorithm will reduce the amount of carbon footprint in the environment.

The development of Recommender Systems (RSs) aims to generate recommendations with high quality, and on the other hand, the privacy of the user is not considered as a significant issue. Especially, when the RS utilizes the cloud platform for the recommendation generation process, the privacy of the user is needed to be preserved to ensure the security of user’s sensitive data. In this paper, we present an Improved MORE approach as a Fully Homomorphic Encryption algorithm to secure user’s data in the cloud environment. To generate secure location recommendations to the users, we present SECure RECommendation SYstem (SECRECSY) framework by protecting user’s sensitive privacy information in the cloud during the recommendation generation process. To meet the increasing demands of group recommendations, we extend our SECRECSY as Group Recommendation Model to suggest POIs to the group of users. The experimental results and findings are helpful to the researchers for developing better RSs for both individual and group users.

In recent days, management of keys in a group has become a significant part of data communication. The textual and alphanumerical passwords used for security concern have now changed its trend to different graphical passwords procedures. Many researchers have introduced various efficient and scalable key management schemes and it is difficult to remember this type of password. There are many textual password authentication mechanisms now available in the software market and are prone to eavesdropping, dictionary attacks and shoulder surfing. To address all the above vulnerability, many researchers and practitioners have developed different authentication methods who are interested in finding an alternate way to the existing problem. Hence, this paper proposes a secure group communication scheme between group members using graphical passwords and this is easy to remember compared to a textual password but difficult to hack. The elliptic curve cryptography technique is applied for key distribution. The group key is a graphical password which is static and shared among all the members in a group. The sequence of images will be sent to the members/users of a group during registration to form a group key and the group controller sends a pass point value of each image to the member by using elliptic curve cryptography after the registration. The main focus of the proposed scheme is to provide better security and ensures negligible communication overhead and computation overhead.

In recent times, the modern developments of internet technologies and social networks have attracted global researchers to explore the recommender systems for generating personalized location-based services. Recommender Systems (RSs) as proven decision support tools have gained immense popularity to solve information overloading problem among various real-time applications of e-commerce, travel and tourism, movies and e-learning. RSs emerge as a popular and reliable information filtering approach that is capable of suggesting relevant items, movies, and locations to the active target user based on dynamic preferences and interests. Beyond the development of many feature-rich recommendation algorithms, the need for a better full-fledged RS to produce precise and highly relevant recommendations based on ratings and preferences provided by the target user is very high. With the specific focus to the travel domain, the global research community has been involved in the development of a complete travel recommender system that is immune to the sparsity and cold start problems. In this paper, we present a new Hybrid Location-based Travel Recommender System (HLTRS) through exploiting ensemble based co-training method with swarm intelligence algorithms to enhance the personalized travel recommendations. The proposed HLTRS is experimentally validated on the real-world large-scale dataset, and we have made an extensive user study to determine the ability of developed RS to produce user satisfiable recommendations in real-time scenarios. The obtained results and analyses demonstrate the improved performance of the proposed Hybrid Location-based Travel Recommender System over existing baselines of recommender systems research.

Diabetes is featured by the high prevalence and low control resulting in high premature mortality rate. Maintaining the blood glucose level can bring considerable medical benefits and reduces the risk of diabetes. In real-time, continuous monitoring of blood glucose level is the major challenge. However, monitoring only glucose level without considering other factors such as ECG and physical activities can mislead to improper medication. Therefore, the ever-growing requirement for omnipresent healthcare system has engaged promising technologies such as the Internet of Things and cloud computing. Utilization of these techniques result with the computational complexity, high latency, and mobility problems. To address the aforesaid issues, we propose an energy efficient fog-assisted healthcare system to maintain the blood glucose level. The J48Graft decision tree is used to predict the risk level of diabetes with higher classification accuracy. By deploying fog computing, an emergency alert is generated immediately for precautionary measures. Experimental results illustrate the improved performance of the proposed system in terms of energy efficiency, prediction accuracy, computational complexity, and latency.

The research and development of recommender systems are traditionally focused on the enhancement and guaranteeing the recommendation accuracy to achieve user satisfaction. On the other hand, the alternative recommendation qualities such as diversity and novelty have received significant attention from researchers in recent times. In this paper, we present a detailed study of the diversity in recommender systems to help researchers in the development of recommendation approaches to generate efficient recommendations. We have also analyzed the existing works for assessment of impact and quality of diversified recommendations. Based on our detailed investigation of the diversity in recommendations, we shift the generic focus from accuracy objectives to explore beyond the accuracy of recommendations. The need for recommender systems producing diversified recommendations without compromising the accuracy is very high to meet the growing demands of users. To address the personalization problem in travel recommender systems, we present the hybrid swarm intelligence clustering ensemble-based recommendation framework to generate diverse and accurate Point of Interest recommendations. Our proposed recommendation approach employs multiple swarm optimization algorithms to frame a clustering ensemble for the generation of efficient user clustering. We have evaluated our proposed recommendation approach over a real-time large-scale dataset of TripAdvisor to estimate the quality of recommendations in terms of diversity and accuracy. The experimental results demonstrate the enhanced efficiency of the proposed recommendation approach over state-of-the-art techniques.

Fall causes serious perils for the elder people when they are living alone. A mathematical model, optimized ANFIS using frequent pattern mining (OAFPM) for activity recognition, is proposed in this paper, which uses fuzzy inference system, adaptive neural network and frequent pattern mining (FPM) to identify the activity of a person accurately. Accelerometer values are given as input to the proposed model in real time which forms the premise part of the model, whereas the consequence is defined by the rules generated out of input and output linear relation. Initial rule identification is done through membership functions of each activity, and the number of rules is reduced using FPM approach. During the learning phase, the optimal premise parameters are selected using gradient descent method and the choice of consequent parameters is based on the least-square estimation method. The optimal values of premise and consequent parameters along with the reduced rule matrix made the OAFPM model to achieve an accuracy rate of 95.8 $$\%$$ and also reduce the computational complexity by triggering less number of nodes for each activity.

The internet of things (IoT) is an internet among things through advanced communication without human’s operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.

In the current article we obtain the extension of Darbo’s fixed point theorem (DFPT), and apply this theorem to prove the existence of solution of an infinite system of implicit fractional integral equations. We, besides that, justify the results with the help of an example. The advantage of the proposed fixed point theory is that the requirement of the compactness of the domain is relaxed which is essential in some fixed point theorems. Also, we have applied it to integral equation involving fractional integral by another function which is a generalization of many fixed point theorems as well as fractional integral equations.

In this paper, we investigate the approximate controllability of fractional evolution inclusions with hemivariational inequalities of order $1< r 1 < r < 2 . The main results of this paper are verified by using the fractional theories, multivalued analysis, cosine families, and fixed-point approach. At first, we discuss the existence of the mild solution for the class of fractional systems. After that, we establish the approximate controllability of linear and semilinear control systems. Finally, an application is presented to illustrate our theoretical results.

The goal of this present paper is to study some new inequalities for a class of differentiable functions connected with Chebyshev’s functionals by utilizing a fractional generalized weighted fractional integral involving another function$\mathcal{G}$Gin the kernel. Also, we present weighted fractional integral inequalities for the weighted and extended Chebyshev’s functionals. One can easily investigate some new inequalities involving all other type weighted fractional integrals associated with Chebyshev’s functionals with certain choices of$\omega (\theta )$ω(θ)and$\mathcal{G}(\theta )$G(θ)as discussed in the literature. Furthermore, the obtained weighted fractional integral inequalities will cover the inequalities for all other type fractional integrals such as Katugampola fractional integrals, generalized Riemann–Liouville fractional integrals, conformable fractional integrals and Hadamard fractional integrals associated with Chebyshev’s functionals with certain choices of$\omega (\theta )$ω(θ)and$\mathcal{G}(\theta )$G(θ).

In our manuscript, we organize a group of sufficient conditions for the approximate controllability of second-order evolution hemivariational inequalities. By applying a suitable fixed-point theorem for multivalued maps, we prove our results. Lastly, we present an example to illustrate the obtained theory.

In recent years, internet technologies and its rapid growth have created a paradigm of digital services. In this new digital world, users suffer due to the information overload problem and the recommender systems are widely used as a decision support tool to address this issue. Though recommender systems are proven personalization tool available, the need for the improvement of its recommendation ability and efficiency is high. Among various recommendation generation mechanisms available, collaborative filtering-based approaches are widely utilized to produce similarity-based recommendations. To improve the recommendation generation process of collaborative filtering approaches, clustering techniques are incorporated for grouping users. Though many traditional clustering mechanisms are employed for the users clustering in the existing works, utilization of bio-inspired clustering techniques needs to be explored for the generation of optimal recommendations. This article presents a new bio-inspired clustering ensemble through aggregating swarm intelligence and fuzzy clustering models for user-based collaborative filtering. The presented recommendation approaches have been evaluated on the real-world large-scale datasets of Yelp and TripAdvisor for recommendation accuracy and stability through standard evaluation metrics. The obtained results illustrate the advantageous performance of the proposed approach over its peer works of recent times.

A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%.

The growing use of wireless technology in healthcare systems and devices makes these systems particularly open to cyber-based attacks, including denial of service and information theft via sniffing (eaves-dropping) and phishing attacks. Evolving technology enables wireless healthcare systems to communicate over longer ranges, which opens them up to greater numbers of possible threats. Unmanned aerial vehicles (UAV) or drones present a new and evolving attack surface for compromising wireless healthcare systems. An enumeration of the types of wireless attacks capable via drones are presented, including two new types of cyber threats: a stepping stone attack and a cloud-enabled attack. A real UAV is developed to test and demonstrate the vulnerabilities of healthcare systems to this new threat vector. The UAV successfully attacked a simulated smart hospital environment and also a small collection of wearable healthcare sensors. Compromise of wearable or implanted medical devices can lead to increased morbidity and mortality.

In recent times, Recommender Systems (RSs) are gaining immense popularity with the wider adaptation to deal information overload problem in various application domains such as e-commerce, entertainment, e-tourism, etc. RSs are developed as information filtering systems to make personalized predictions based on the priorities and preferences for the suggestion of relevant items to users. Travel Recommender Systems (TRSs) generates a list of best matching locations or Point of Interests (POIs) to the users based their preferences. Predicting interesting locations for the generation recommendations from Location Based Social Network (LBSN) is crucial due to variety, size, and dimensions of data. The growing demand for effective TRS extends the scope for the development of user behavior based recommendation approach. In the literature, several research works are conducted to generate location recommendations by focusing on location attributes and failed to incorporate user behavior. As a significant solution to the existing limitations of TRSs, we propose Activity and Behavior induced Personalized Recommender System (ABiPRS) as a hybrid approach to predict persuasive POI recommendations. The proposed ABiPRS is designed to support travelling user by providing effective list of POIs as recommendations. As an extension, we have designed a new group recommendation model to meet the requirements of the group of users by exploiting relationships between them. Further, we have developed a novel hybridization approach for aggregating recommendations from multiple RSs to improve the effectiveness of recommendations. The proposed approaches are evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed hybrid recommendation approach over standalone and baseline hybrid approaches.

In the present era of Information Technology, almost all big and small scale companies are moving towards cloud to store and manage the data. Cloud computing is a routine of deploying a structure of distant servers speed up on the Internet to store, oversee, and manage information, rather than a neighbourhood server or a PC. The purpose for cloud procurement is reduced cost, adaptability, regular access and refreshed programming. Nowadays, healthcare frameworks are adjusting computerized stages and ending up being more patient-centered and data driven. In this paper, we present a planning empowered intermediary re-encryption method to defeat the security issues. This Technique will allow only limited access rights to an authorized agent to access the records for a specific time period. This technique will use a searchable encryption and proxy Re-encryption technique.

The recent developments of internet technology have created premium space for recommender system (RS) to help users in their daily life. An effective personalized recommendation of a travel recommender system can reduce time and travel cost of the travellers. ProTrip RS addresses the personalization problem through exploiting user interests and preferences to generate suggestions. Data considered for the recommendations include travel sequence, actions, motivations, opinions and demographic information of the user. ProTrip is completely designed to be intelligent and in addition, the ProTrip is a health-centric RS which is capable of suggesting the food availability through considering climate attributes based on user’s personal choice and nutritive value. A novel functionality of ProTrip supports travellers with long-term diseases and followers of strict diet. The ProTrip is built on the pillars of ontological knowledge base and tailored filtering mechanisms. The gap between heterogeneous user profiles and descriptions is bridged using semantic ontologies. The effectiveness of recommendations is enhanced through a hybrid model of blended filtering approaches, and results prove that the proposed ProTrip to be a proficient system. The developed food recommendation approach is evaluated for the real-time IoT-based healthcare support system. We also present a detailed case study on the food recommendation-based health management. The proposed system is evaluated on real-time dataset, and analysis of the results shows improved accuracy and efficiency compared to existing models.

The change in the DNA is a form of genetic variation in the human genome. In addition, the DNA copy number change is also linked with the progression of many emerging diseases. Array-based Comparative Genomic Hybridization (CGH) is considered as a major task when measuring the DNA copy number change across the genome. Moreover, DNA copy number change is an essential measure to diagnose the cancer disease. Next generation sequencing is an important method for studying the spread of infectious disease qualitatively and quantitatively. CGH is widely used in continuous monitoring of copy number of thousands of genes throughout the genome. In recent years, the size of the DNA sequence data is very large. Hence, there is a need to use a scalable machine learning approach to overcome the various issues in DNA copy number change detection. In this paper, we use a Bayesian hidden Markov model (HMM) with Gaussian Mixture (GM) Clustering approach to model the DNA copy number change across the genome. The proposed Bayesian HMM with GM Clustering approach is compared with various existing approaches such as Pruned Exact Linear Time method, binary segmentation method and segment neighborhood method. Experimental results demonstrate the effectiveness of our proposed change detection algorithm.

Electricity usage at electricity rush hour (peak hour) may vary from each and every service area such as industrial area, commercial area and residential area. Equalizing the power consumption in industry may lead to the utilization of power in other service areas in an efficient way. Although industries have comparably lesser number of power consuming device types than other service areas the power consumption is quite high. To meet the demands rising in the industry, shiftable loads (devices) can be redistributed equally to all the working time slots based on the average power utilization. It can be done in a flexible manner by shaping the loads using Demand Side Management (DSM) technique in Smart Grid. The main objective is to minimize the power utilization during the electricity rush hour by effectively distributing the power available during off-peak hour. Evolutionary algorithm can be well adapted to problems where optimization is the core criteria. Any maximization or minimization problem can be solved efficiently using evolutionary algorithm. Hence, to obtain the optimized fitness function of load redistribution in industry Genetic Algorithm in Demand Side Management (GA-DSM) is chosen and it has benefited with an overall reduction of 21.91% which is very remarkable. In addition to this the evaluation of the fitness function using GA-DSM is carried out in other two industrial dataset models (steel plant and wind power plant) which is unavailable so far in the literature.

In this paper, we are dealing with the approximate controllability for a class of second-order stochastic evolution inclusions of Clarke’s subdifferential type. Initially, we show the existence of mild solutions for the stochastic evolution inclusions by using stochastic analysis, nonsmooth analysis and fixed point theorems of multivalued maps. Then we provide a set of sufficient conditions for the approximate controllability of the second-order stochastic evolution inclusions. Finally, an example is included for the illustration of the obtained theoretical results.

Mixed carbide (70 % Pu, 30 % U) spent fuels up to a burn up of 155 GWd/Te and a cooling time of around 2 years discharged from the Fast Breeder Test Reactor (FBTR) were successfully reprocessed at COmpact Reprocessing of Advanced fuels in Lead shielded (CORAL) facility in India at Kalpakkam. The operational experience accumulated is translated to design of the future reprocessing plants such as Demonstration Fast Reactor Fuel Reprocessing Plant (DFRP) and Fast Reactor Fuel Reprocessing Plant (FRP). Concurrent R&D activities have been conducted to optimize process steps for reprocessing fast reactor fuels.

Substituted 4-arylamino-1,3-dioxanes were synthesized using a new synthetic protocol of the Prins reaction between aryl amines and acetaldehyde. This one-pot synthesis occurs under catalyst-free conditions in an aqueous medium. By employing condensation of excess acetaldehyde with an aromatic amine in water at 0–5 °C, the corresponding N-aryl-2,6-dimethyl-1,3-dioxan-4-amines are obtained in good yields. Consequently, this is an environmentally benign, simple, efficient, “green” procedure for the preparation of 1,3-dioxanes.

In this article, we consider a class of second-order impulsive evolution differential equations with nonlocal conditions. This article deals with the nonlocal controllability for a class of second-order evolution impulsive control systems. We prove some sufficient conditions for controllability using the measure of noncompactness and Monch fixed point theorem. Very particularly we do not assume that the evolution system generates a compact semigroup. Finally, an example is given to represent the obtained theory.