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Hardy-Leindler type inequalities and their converses for multiple integrals on time scales are proved by using Fubini’s theorem and induction principle. Some generalized versions of Hardy, Wirtinger and Leindler inequalities in both continuous and discrete cases are also derived in seek of applications.

In the present study, a novel application of integrated evolutionary computing paradigm is presented for the analysis of nonlinear systems of differential equations representing the dynamics of virus propagation model in computer networks by exploiting the discretization strength of finite difference procedure, global search efficacy of genetic algorithms (GAs) aided with interior-point method (IPM) as efficient local search mechanism. Residual error based cost function is constructed by utilizing the effectiveness of approximation in mean square error sense and combined strength of GA-IPM is used as a viable optimization mechanism to find the solution of the problem. The proposed scheme is implemented for dynamical analysis of the model in terms of susceptible-infected and protected computer nodes by varying the probabilities of infections, countermeasures, curing and immunity while keeping connected and disconnected rates fixed. The statistical performance evaluated by means of deviations from reference Adams numerical results are practiced viably through performance metrics of accuracy and computational complexity to demonstrate the worth of integrated stochastic solver.

Skin cancer is measured as one of the fatal types of cancer diseases in humans, among numerous kinds of malignancy. Current diagnostic classifications are lacking in finding an effective treatment. The effective and early stage treatment of skin disease can increase the survival rate of patients. Substantial investigative work has been developed to improve computer aided diagnosis system to detect cancer at early stage. However, early detection of skin cancer still requires better accuracy through experiment on digital skin lesion images as a multiclass classification, rather than using biopsy methods. This paper presents an intelligent framework to detect and classify four types of skin cancer. Before classification, noise removal from skin lesion is performed by gaussian filter. Textural and colour features are extracted from skin lesion to detect and classify cancer into four types. Support vector Machine is trained to classify Melanoma, Nevus, Basal and Squamous skin cancer types. Extensive experiments are performed on standard benchmark skin cancer images dataset with an improvement in accuracy of 92.41% after comparison with the well-known methods.

In this paper, Jensen’s inequality and Fubini’s Theorem are extended for the function of several variables via diamond integrals of time scale calculus. These extensions are used to generalize Hardy-type inequalities with general kernels via diamond integrals for the function of several variables. Some Hardy Hilbert and Polya Knop type inequalities are also discussed as special cases. Classical and new inequalities are deduced from the main results using special kernels and particular time scales.

In the paper we extend some Hardy and Littlewood type inequalities on time scales for the function of n variables. Special cases of obtained results include generalized Wirtinger, Hardy and Littlewood type inequalities.

In this paper, Petrović’s inequality is generalized for \(h−\)convex functions on coordinates with the condition that \(h\) is supermultiplicative. In the case, when \(h\) is submultiplicative, Petrović’s inequality is generalized for \(h−\) concave functions. Also particular cases for \(P−\) function, Godunova-Levin functions, \(s−\) Godunova-Levin functions and \(s−\) convex functions has been discussed.

Nowadays, both the modern and developing countries are planning to deploy Internet of Vehicles for smart transportation systems to face the traffic congestion problems. Shifting from current road conditions to smart ones is a real challenge where all the vehicles and road infrastructure are not equipped with wireless network interfaces. The urban environment is more congested than highway scenarios and has more issues of delay and travel time. Various types of route suggestion protocols have been proposed where most of them consider smart vehicles which are equipped with smart and intelligent communication devices. The most of existing schemes have not considered non-equipped vehicles which have a major cause of traffic congestion. This paper presents a route suggestion protocol to suggest an optimal congestion aware route in the network. Equipped and non-equipped vehicles are taking into account for congestion index formulation along with driving distraction factors. Simulation results indicated the greater performance of proposed protocol and reduce the travel time effectively when works with IoV as compared to traditional route suggestion protocols.

In the past few decades, research has been carried out to automatically find humans in a video sequence. Automatically detecting humans in videos is gaining interest for numerous applications such as driver assistance system, security, people counting, human gait characterization, video annotations, retrieval, or crowd flow analysis. Manual annotation of a video is a time-consuming task that involves human annotators which varying biases. In this paper, we have presented three computer vision algorithms (contour-based, HOG-based and SURF-based) and proposed a deep learning technique that automatically extracts spatiotemporal annotations of human and represents it by a bounding box. We have performed experiments and the accuracy obtained for each method is 86%, 92.5%, 94%, and 95.5%, respectively. Results show that not only annotation accuracy has increased but the human effort has reduced with respect to manual annotations. We have also introduced a new dataset ASSVS_KICS which is captured through a high-quality stationary camera and contain scenarios based on our community for video surveillance research.

The departments of transportation worldwide are facing various challenges despite introducing and incorporating various vehicular features. One of such challenges is to make vehicles autonomous, intelligent, and capable of self-learning to evolve their knowledge repository. In this paper, human cognition is proposed to be implemented in vehicles so that they can perform human-like decisions. Therefore, the process of vehicular route decision is debated cognitively in order to provide route information intelligently. The in-vehicle routes provided by the GPS are not optimal and lack on-demand user requirements. GPS connectivity issues, in certain conditions, make it difficult for vehicles to take real-time decisions. This leads to the idea of self-decision by the vehicle controller. We propose a cognitive framework for vehicles to make self-decisions that use cognitive memory for storing route experiences. The framework strengthens the existing in-vehicle route finding capability and its provision in a more realistic manner. The user is provided with all available route-related information that is required for the journey. In addition, the route episodes are learned, stored, and accessed inside the cognitive memory for an optimal route provision. The vehicle learns about the routes and matures with route-experience by itself with the passage of time. In simulations, fuzzy modeling is used to validate the impact of cognitive parameters over static/conventional parameters. Moreover, artificial neural networks are used to minimize the error rate in learning to achieve cognitive route decisions. The proposed in-vehicle cognitive framework outperforms the existing route provision system that is inadequate and provokes the user's anxieties during driving. Besides, the proposed scheme gradually gets mature in delivering optimal as well as latest route-related information.

Alzheimer's Disease (AD) is the most common form of dementia. It gradually increases from mild stage to severe, affecting the ability to perform common daily tasks without assistance. It is a neurodegenerative illness, presently having no specified cure. Computer-Aided Diagnostic Systems have played an important role to help physicians to identify AD. However, the diagnosis of AD into its four stages; No Dementia, Very Mild Dementia, Mild Dementia, and Moderate Dementia remains an open research area. Deep learning assisted computer-aided solutions are proved to be more useful because of their high accuracy. However, the most common problem with deep learning architecture is that large training data is required. Furthermore, the samples should be evenly distributed among the classes to avoid the class imbalance problem. The publicly available dataset (OASIS) has serious class imbalance problem. In this research, we employed a transfer learning-based technique using data augmentation for 3D Magnetic Resonance Imaging (MRI) views from OASIS dataset. The accuracy of the proposed model utilizing a single view of the brain MRI is 98.41% while using 3D-views is 95.11%. The proposed system outperformed the existing techniques for Alzheimer disease stages.

In this study, we extend some “sneak-out” inequalities on time scales for a function depending on more than one parameter. The results are proved by using the induction principle and time scale version of Minkowski inequalities. In seeking applications, these inequalities are discussed in classical, discrete, and quantum calculus.

A vehicular adhoc network (VANET) recently emerged in the the Internet of Vehicles (IoV), it involves the computational processing of moving vehicles. Nowadays, IoV has turned into an interesting field of research as vehicles can be equipped with processors, sensors, and communication devices. IoV gives rise to handoff, which involves changing the connection points during the online communication session. This presents a major challenge for which many standardized solutions are recommended. Although there are various proposed techniques and methods to support seamless handover procedure in IoV, there are still some open research issues, such as unavoidable packet loss rate and latency. On the other hand, the emerged concept of edge mobile computing has gained crucial attention by researchers that could help in reducing computational complexities and decreasing communication delay. Hence, this paper specifically studies the handoff challenges in cluster based handoff using new concept of dynamic edge-backup node. The outcomes are evaluated and contrasted with the network mobility method, our proposed technique, and other cluster-based technologies. The results show that coherence in communication during the handoff method can be upgraded, enhanced, and improved utilizing the proposed technique.

Information-centric networking is the developing model envisioned by an increasing body of the data communication research community, which shifts the current network paradigm from host centric to data centric, well-known to information-centric networking (ICN). Further, the ICN adopts different types of architectures to extend the growth of the Internet infrastructure, e.g., name-based routing and in-network caching. As a result, the data can be easily routed and accessed within the network. However, when the producer generates contents for authentic consumers, then it is necessary for him/her to have a technique for content confidentiality, privacy, and access control. To provide the previously mentioned services, this paper presents a certificateless proxy reencryption scheme (CPRES) based on the hyperelliptic curve for access control in the content-centric network (CCN). Using certificateless PRE, the power of the key generation center (KGC) is limited to only the generation of partial keys to secure the access to the content. With the help of these partial keys, the producer further calculates keys for encryption and reencryption process. The simulation results show that the proposed scheme provides secure access to content during end-to-end communication. Moreover, the proposed CPRES scheme outperforms in terms of low computational energy and efficient utilization of communication bandwidth.

Malware detection is an important factor in the security of the smart devices. However, currently utilized signature-based methods cannot provide accurate detection of zero-day attacks and polymorphic viruses. In this context, an efficient hybrid framework is presented for detection of malware in Android Apps. The proposed framework considers both signature and heuristic-based analysis for Android Apps. We have reverse engineered the Android Apps to extract manifest files, and binaries, and employed state-of-the-art machine learning algorithms to efficiently detect malwares. For this purpose, a rigorous set of experiments are performed using various classifiers such as SVM, Decision Tree, W-J48 and KNN. It has been observed that SVM in case of binaries and KNN in case of manifest.xml files are the most suitable options in robustly detecting the malware in Android devices. The proposed framework is tested on benchmark datasets and results show improved accuracy in malware detection.

Wireless networking is experiencing a tremendous growth in new standards of communication and computer applications. Currently, wireless networks exist in various forms, providing different facilities. However, due to some limitations as compared with wired counterparts, wireless networks face several major challenges and one of them is optimum bandwidth allocation. The focus of optimum bandwidth allocation is to reduce the losses and satisfying quality of service (QoS) requirements. In wireless networks, the term bandwidth allocation is attributed as the distribution of bandwidth resources among different users, which affects the serviceability of the entire system. Though many studies related to bandwidth allocation have been reported already, however, only sub-optimal solutions have been provided so far. In this research, we proposed to use the differential evolution (DE) algorithm to allocate bandwidth through a bandwidth reservation scheme in the Cellular IP network, in order to improve the QoS at an acceptable level. DE belongs to a class of evolutionary algorithms (EA), like particle swarm optimization and genetic algorithm. A DE-based method is used which looks for any free bandwidth in the cell or in adjacent cells and provides it to the cell where required. In case, it fails to find the free available bandwidth then it will search the bandwidth which is standby for non real-time users and allocates it to the real-time users that will help in improving the QoS in terms of connection/call dropping probability for real-time users. Simulation results show that the proposed method performs better as compared to previously used EA models for bandwidth allocation.

A Wireless Mesh Network (WMN) can provide Internet connectivity to end users through heterogenous access network technologies. However, the mobility of mobile nodes across these access networks in WMNs results in service disruption. Existing mobility management protocols are designed for single hop networks and are centralized in nature. A Distributed IP-based Mobility Management Protocol (DIMMP) is proposed in this paper that provides seamless mobility with service continuation for mobile nodes when they roam across WMNs. Instead of relying on a centralized mobility anchor, the mobility functionality is distributed at multiple nodes in the WMN, in order to reduce the chances of potential single point of failure. The proposed protocol manages both types of mobilities i.e. intra-WMN and inter-WMN and uses a new enhanced route optimization procedure. Simulation results show that this work has contributed by improving the performance of handoff procedure with respect to handoff latency, packet loss and signalling overhead, as compared to the existing protocols.

Nodes in a multi-hop wireless network rely on each other to maintain network connectivity. A primary design objective of these networks is to eliminate or minimize the unavailability of network at some point. However, the movement of nodes creates service disruption and delay as path failure detection and re-establishment consume considerable amount of time. Existing solutions for link classification and prediction operate on discrete values resulting in performance degradation. This problem can best be modelled using fuzzy logic. This work presents a new fuzzy logic based link status classification mechanism for the use with multipath routing protocols in multi-hop wireless networks. The new Enhanced Adhoc On-demand Multipath Distance Vector (eAOMDV) uses fuzzy logic to classify the link status as active, about-to-break or broken. On the basis of fuzzy input parameters RSSI, velocity, distance and bit error rate, the fuzzy based classification mechanism predicts in advance that a particular link, on the active path between the sender and receiver, is about-to-break and helps the reactive multipath routing protocol to start the rerouting operation, thus minimizing the service disruption time, route discovery frequency, end-to-end delay and the packet loss.

Nowadays, because of the unpredictable nature of sensor nodes, propagating sensory data raises significant research challenges in Wireless Sensor Networks (WSNs). Recently, different cluster-based solutions are designed for the improvement of network stability and lifetime, however, most of the energy efficient solutions are developed for homogeneous networks, and use only a distance parameter for the data communication. Although, some existing solutions attempted to improve the selection of next-hop based on energy factor, nevertheless, such solutions are unstable and lack a reducing data delivery interruption in overloaded links. The aim of our proposed solution is to develop Reliable Cluster-based Energy-aware Routing (RCER) protocol for heterogeneous WSN, which lengthen network lifetime and decreases routing cost. Our proposed RCER protocol make use of heterogeneity nodes with respect to their energy and comprises of two main phases; firstly, the network field is parted in geographical clusters to make the network more energy-efficient and secondly; RCER attempts optimum routing for improving the next-hop selection by considering residual-energy, hop-count and weighted value of Round Trip Time (RTT) factors. Moreover, based on computing the measurement of wireless links and nodes status, RCER restore routing paths and provides network reliability with improved data delivery performance. Simulation results demonstrate significant development of RCER protocol against their competing solutions.

In the paper, some variants of Montgomery identity with the help of delta and nabla integrals are established which are useful to produce Montgomery identity involving alpha diamond integrals for function of two variables. The aforementioned identity is discussed in discrete, continuous, quantum calculus as well and employed to obtain Ostrowski type inequality for monotonically increasing function with respect to both parameters.

The paper proposes a sensors platform to control a barrier that is installed for vehicles entrance. This platform is automatized by image-based license plate recognition of the vehicle. However, in situations where standardized license plates are not used, such image-based recognition becomes non-trivial and challenging due to the variations in license plate background, fonts and deformations. The proposed method first detects the approaching vehicle via ultrasonic sensors and, at the same time, captures its image via a camera installed along with the barrier. From this image, the license plate is automatically extracted and further processed to segment the license plate characters. Finally, these characters are recognized with the help of a standard optical character recognition (OCR) pipeline. The evaluation of the proposed system shows an accuracy of 98% for license plates extraction, 96% for character segmentation and 93% for character recognition.

The Internet of vehicles (IoV) is a newly emerged wave that converges Internet of things (IoT) into vehicular networks to benefit from ubiquitous Internet connectivity. Despite various research efforts, vehicular networks are still striving to achieve higher data rate, seamless connectivity, scalability, security, and improved quality of service, which are the key enablers for IoV. It becomes even more critical to investigate novel design architectures to accomplish efficient and reliable data forwarding when it comes to handling the emergency communication infrastructure in the presence of natural epidemics. The article proposes a heterogeneous network architecture incorporating multiple wireless interfaces (e.g., wireless access in vehicular environment (WAVE), long-range wireless fidelity (WiFi), and fourth generation/long-term evolution (4G/LTE)) installed on the on-board units, exploiting the radio over fiber approach to establish a context-aware network connectivity. This heterogeneous network architecture attempts to meet the requirements of pervasive connectivity for vehicular ad hoc networks (VANETs) to make them scalable and adaptable for IoV supporting a range of emergency services. The architecture employs the Best Interface Selection (BIS) algorithm to always ensure reliable communication through the best available wireless interface to support seamless connectivity required for efficient data forwarding in vehicle to infrastructure (V2I) communication successfully avoiding the single point of failure. Moreover, the simulation results clearly argue about the suitability of the proposed architecture in IoV environment coping with different types of applications against individual wireless technologies.

Internet of Things (IoT) is an emerging technology that offers promising solutions to modernize the traditional systems. It accords promising result in crystallizing smart cities, smart homes, smart industries, and smart environment. This article presents the smart waste management architecture for smart cities and efficient routing technique considering least delay for the architecture. In wireless sensor networks, end-to-end delay is one of the important Quality of Services (QoS) parameter to overcome delay in data communication. In this article, we consider end-to-end delay minimization in smart waste management application. The term “end-to-end delay” is defined as the total time taken by a single packet to reach the destination node. The proposed scheme considers the interference level, the length of the routing path, and the number of hops along the path. The simulation results show that the proposed scheme outperforms current schemes.

Underwater Wireless Sensor Networks (UWSNs) contain several components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection tasks. These networks are used interactively between different nodes and ground-based stations. Presently, UWSNs face issues and challenges regarding limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. In the last few decades, research community provided different methodologies to overcome these issues and challenges; however, some of them are still open for research due to variable characteristics of underwater environment. In this paper, a survey of UWSN regarding underwater communication channel, environmental factors, localization, media access control, routing protocols, and effect of packet size on communication is conducted. We compared presently available methodologies and discussed their pros and cons to highlight new directions of research for further improvement in underwater sensor networks.

The aim of this paper is to give some companion inequalities to the Ostrowski-Grϋss type inequality for n-time differentiable absolutely continuous functions by using recently obtained bounds for the Chebyshev functional.

ATM machines are rapidly developing and are finding new applications from bill payments to online fund transfers. Everything is getting smart in the modern world and ATM are no exceptions in this regard. The natural choice of manufacturers for making ATM smart is by making their function connected to android based system. However, android based Applications are inherently vulnerable and can be exploited by external attackers or by internal malicious users. This paper will discuss the vulnerabilities which are present in android based ATM and the techniques for fixing those vulnerabilities in Android based ATM machines.