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Inflammation is known to be the root cause of the development of coronary artery disease (CAD). Subclinical inflammation is one of the key players in pathophysiology of CAD. An inflammatory response leads to the secretion of various adipokines and cytokines. There are various pro-inflammatory markers which could be used to determine the onset of disease, and one such marker is resistin adipokine. The present study evaluates and compares the circulatory concentration of resistin in CAD patients and controls for the determination of its association with CAD. In this study, 58 CAD patients were enrolled and were further subdivided into four groups as per the associated co-morbidities. Various anthropometric and biochemical parameters were assessed. The circulatory levels of resistin were found to be significantly elevated in CAD patients and their subgroups as compared to controls. We conclude that elevated resistin levels in CAD patients suggest a key role of resistin in the development of CAD. The present study also observed that co-morbidities like type 2 diabetes can act as one of the major factors for the elevated concentration of resistin in the circulation of CAD patients with diabetes.

Blockchain technology permits a highly secured record keeping and digital transaction. Blockchain technology is changing the digital world and industry by bringing a new view to security, transparency, and efficiency of systems. It provides a safe way for the exchange of products, services, or transaction. Blockchain will enable more agile value chains, faster product innovations, closer customer relationship sector. This paper provides an overview of blockchain technology and its potential in developing a secure and reliable agriculture supply chain management. Agriculture supply chain management systems are vital for getting food products delivered from farmers to the consumers. Blockchain technology can also be used to achieve better prices and payment options, land title registration and for transparent disbursement of subsidies to farmers.

Chronic Kidney Disease (CKD) is a global health threat that affects approximately 10% of the global population and approximately 20% of the Indian population. Medical specialists face a large and difficult burden in detecting this disease at an early stage. In this case, a computer-based intelligent system that can diagnose kidney disease and its stages could be unbelievably valuable. Artificial Intelligence (AI) is being used by researchers and scientists to create smart solutions. This paper gives a gist of the different varieties of works done by different scholars in the field of early prediction of CKD using AI techniques. Further, all the works are categorized into 3 different categories and further it has been attempted to analyze the best proposals in those categories. Finally, it has been concluded with the most optimizing and exhaustive proposal.

Big data analytics has given us the ability to analyze massive amounts of data, but it has also put privacy at a risk. Personal information may be present in data processed by big data analytics, which must be preserved. Existing privacy preservation strategies, such as anonymization, necessitate dividing a dataset into a set of sensitive, quasi-identifiers and non-sensitive properties. It may be conceivable to have such a distribution with structured data, but it is difficult to detect sensitive features and quasi-identifiers in unstructured data. Big data generates massive amounts of data that are heterogeneous in nature and includes a range of different sorts of data, including text, image, audio and video. Traditional data extraction technologies are ineffective in the context of this massive influx of unstructured big data. This paper present a survey of various information extraction techniques for unstructured big data analytics with a context of privacy preservation.

Planning of crops for the next season has been a tedious task for the farmers as it is a difficult prediction about metrics of prices that their crop will fetch in a particular season which will be typically based on dynamic weather conditions. This leads to inaccurate prediction of crops’’ prices by farmers, and they happen to wrongly select the crops or in haste they happen to sell their crops early without storing and thus earning less than what the same crop would have fetched them in the future. This problem could be addressed by an ML model which will predict the prices of crops in advance showing the proper analysis of the crop and presenting their future scenario so that farmers can select the right crops to strategize crop production which involves crop selection, time of sowing deciding crop pattern and storage of harvested crops providing enough insights for predicting the appropriate price in the markets.

With the substantial increase in the internet usage and the growing threat of hackers to infect as many devices as possible, security has become important to prevent data breaches and industrial sabotage. Unified Threat Management are the Next-Generation network security appliances that include multiple security features along with the performance required for future networks. But packet processing usually consumes 70% of the CPU time and during heavy load it degrades the UTM performance by dropping important packets. To overcome such limitations, many techniques and algorithms have been proposed by the researchers. In this paper, survey and numerical analysis of each technique is done based on the overall packet processing time. Based on the numerical analysis, we suggest the best technique to reduce the overall packet processing time in UTM and hence reduce the load under heavy traffic conditions.

The usage of online payment systems is increasing multifold these days due to their transaction reliability and ease of completion in real-time. The most preferred service is UPI service, which is majorly available in Metropolitan cities, in some semi-urban and rural areas which have a proper communication infrastructure. However, there is a major issue to expand these services in rural areas, which lack proper communication infrastructure. Even after the internet revolution, it is ensured that all Indians are connected to data at all times but there might be situations where the user could not able to connect to the Internet owing to many reasons like High Altitude, Low Network availability, in-flight, etc. As an individual, such situations cannot be avoided, and there it comes to the applicability of offline transaction systems. We plan to change that by introducing an offline transaction system. We plan to expand on the idea of an offline payment system that will ensure that a reliable & secured digital payment system is available to all individuals.

One of the largest growing industries in the modern-day world is the sporting industry. Currently valued at around 500 billion USD, with a growth scope of exponential potential, its ability to attract investors is incredible. And just like any other investment. It is part andparcel of the investor’s fiscal responsibility to take good care of their assets. The biggest assets in the sporting industry are of course the players, and the greatest threat to said assets is injuries. We take into consideration said factors and deem it important to solve said issues, and understanding the money involved, the industry sides with us too. We seek to solve the said problemby taking into account all previous injury records and datasets of various players and predicting the kind, number, and severity of the injuries in the future. We seek to create a methodology for such prediction, which applies to all and any sports, being one of the only such models of its kind.

Security defies one of the galactic barrier when pondering the use of cloud services. Data outsourcing in the cloud (DOC) is conventionally based on data encryption, which imparts impregnable security, but deteriorating the efficiency and rimming the functionalities of cloud. In this proposal we aim to possess them by proposing a secret sharing scheme that depends on partitioning the data and distributed storage over multi-clouds. The scheme used in the proposal uses a combination of symmetric (AES) and asymmetric (RSA) encryption techniques to share the data among the peers in the cloud environment. The proficiency of the suggested scheme has been demonstrated by the empirical results.

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.

Internet of Things (IoT) is a network of smart devices which communicate with each other to accomplish task or provide any particular service. In IoT, smart objects have to provide services continuously by realizing requirement of a person without disturbing him. To accomplish this task, IoT devices need to share information with each other as soon as it updates. In such condition, request-response technique creates overhead on the system. So publish-subscribe technique is used to get an immediate response as soon as data changes. Service consumer or subscriber needs to send a request for particular service to the broker. The broker will maintain the list of the subscriber and will forward data to all subscribers as it updates. But this broker creates single point bottleneck in publish-subscribe communication. In this paper, distributed publish-subscribe techniques has been proposed which will distribute publishers and subscribers on the different brokers removing single point bottleneck. It will also distribute the load of brokers and improve the availability of service. For simulation purpose, we have used QualNet 5.1 simulation tool. Simulation is done by using parameters like number of total packets received by all nodes in simulation, end to end delay as number of nodes increases also number of total packets received by all nodes in simulation as number of transaction increases.

Increasing popularity of social media sites (e.g., Twitter, Facebook) leads to the increasing amount of online data. Student's casual talks on these social media sites can be used to know their educational experiences i.e. their worries, opinions, feelings, and emotions about the learning process. However, the main challenge is to analyze this informal data. Because of the complexity of the student's posts on the social media sites, it requires human understanding. But, it is practically not possible to perform human interpretation on such increasing scale of data. Hence, there is a demand for pre-programmed data analysis methods. In this paper, a hybrid approach is used to analyze these large scale and informal data. We have considered engineering student's Twitter posts to know their concerns and difficulties in their learning experiences. We have carried out a qualitative analysis on the data of about 25,000 tweets of engineering student's educational problems and studied that many students face problems such as heavy study load, diversity problem, lack of social engagement, their negative emotions and lack of sleep. Considering these analysis, we have implemented a hybrid model by combining two most popular machine learning algorithms viz. Naive Bayes classifier and Support Vector Machine to classify tweets regarding student's problems. The result shows that there is a significant reduction in training time and shows an increase in the classification accuracy when compared to individual Naive Bayes classifier and Support Vector Machine.

Wireless Body Area Network (WBAN) plays an important role in many health related applications by transmitting patients data to central database of hospital. So, routing of data is an important part of WBAN. As WBAN has unique features and requirements, there are crucial issues like Quality of service (QoS), path loss, temperature rise, energy efficiency and network lifetime etc. which are needed to be considering while designing routing protocol. To overcome those issues, we propose hybrid routing protocol which support all WBAN data packets such as delay sensitive data (DSD), normal data (ND), critical data (CD) and reliability sensitive data (RSD) packets. For simplicity in classifying these data packets, we design new data classifier which takes minimum time for classification in order to improve network lifetime. While selecting the next hop we also consider route which have high link quality and low path temperature to solve issue of path loss and temperature rise respectively. NS-2 Simulator has been used for simulation of the proposed protocol.

To avoid transmission collisions in vehicular ad hoc networks, a efficient and reliable medium access control protocol is needed. In VANET, vehicle act as a nodes and vehicle is having high mobility. There are different MAC protocols considering for the communication between the VANET.

Internet of Things (IoT) is a collection of smart devices which communicate with each other to accomplish task or provide any particular service. Machine to Machine (M2M) communication is more in IoT than normal internet. The Internet provides services when any person requests it. In case of IoT, smart devices have to provide services continuously by realizing requirement of a person without disturbing him. To accomplish this task, IoT devices need to share information with each other as soon as it updates. In such condition, request-response technique creates overhead on the system. So publish-subscribe technique is used to get an immediate response as soon as data changes. Service consumer or subscriber needs to send a request for particular service to the broker. The broker will maintain the list of the subscriber and will forward data to all subscribers as it updates. But this broker creates single point bottleneck in publish-subscribe communication. In this report, distributed publish-subscribe techniques has been proposed which will distribute publishers and subscribers on the different brokers removing single point bottleneck. It will also distribute the load of brokers and improve the availability of service.

Internet of Things (IoT) is one of the most buzzing and discussed topic in research field today. Some of the researchers are also looking future of the world in this technology. Since then significant research and development have taken place on IoT, however various vulnerabilities are observed which shall keep IoT as a technology in danger. As a result, there are so many attacks on IoT have been invented before actual commercial implementation of it. The present study discusses about various IoT attacks happening, classify them, its countermeasures and finding the most prominent attacks in IoT. A state of the art survey about the various attacks have been presented and compared including their efficiency and damage level in IoT.

The Vehicular Ad hoc Network (VANET) is the network of the mobile nodes which are connected without infrastructure. The mobile nodes requires routing that works with multi-hop characteristics. The clustering of node is needed in VANET. A cluster head is the essential part of any cluster for the efcient working of the cluster. In this work, we provide a method for the dynamically changing VANET, that elects the cluster head of the clusters and uses different parameters for the same. The QoS parameters are taken care while selecting the cluster head. The parameters involved for the same are direction of the vehicle, Speed of the vehicle, Duration for which it has been cluster head in past, Density of the Cluster, Packet Delivery ratio, Network Lifetime, Degree of the node, Transmission Range. The method proposed is the agglomerate method which involves all the parameters majorly affecting the VANET. Thus, this method will select the cluster head effectively and improves the QoS parameters of the VANET.

Increase in average lifespan and aging population with increase in chronic diseases, there is need to monitor patient health status anywhere both in hospital and out of hospital. This demands more advance health care technology. Due to tremendous growth in sensor technology, the healthcare applications are increasing by manifolds. These opportunities have initiated many research proposals under healthcare domain. Due to its widespread requirement, sensor technology has subset into a category called Wireless Body Area Network (WBANs). These applications are devised to take care of emergency requirements to help the person to handle emergency health issue. To overcome this research problem, many research studies are going on and some already presented. Medium Access Control (MAC) for emergency handling, recently proposed Extended Period and Mixed Period (MPEP) approach overcome the problems associated with 802.15.4 protocol. The MPEP approach is based on superframe structured MAC protocols by considering the contention free period (CFP) allocations. However the problem with this method is inefficient slot allocation technique which may leads to delay in emergency handling in WBAN. To overcome this problem, in this paper MPEP is extended by adding the preemptive slot allocation method for improving the performance of slot allocation and hence emergency handling in WBAN, which is termed as Hybrid MPEP (HMPEP). To verify numerical results, we implemented set up and simulated using NS-2.

Many network security applications such as Intrusion Detection System (IDS), Firewall and Data Loss Prevention System (DLPS) are based on deep packet inspection, in this packets header as well as payload of the packets are checked with predefined attack signature to identify whether it contains malicious traffic or not. To perform this checking different pattern matching methods are used by NIDS. The most popular method to implement pattern matching is to use of Finite Automata (FA). Generally, regular expressions are used to represent most of the attack signatures defined by NIDS. They are implemented using finite automata, which takes the payload of packet as input string. However, existing approaches of Finite Automata (FA), both deterministic finite automata (DFA) and non-deterministic finite automata (NFA) for pattern matching are having their own advantages and some drawbacks. The DFA based pattern matching methods are fast enough but require more memory. However, NFA based pattern matching methods are comparatively takes less memory but the speed of matching is very slow, to overcome these drawbacks of finite automata there are many approaches have been proposed. This paper discuses comparative study of some Finite Automata (FA) based techniques for pattern matching in network intrusion detection system (NIDS).

Uses of multimedia on video and audio application are increasing day-by-day on mobile devices. The continuity of these applications may hamper due to improper session rates during transmission. In this study we survey various papers for session rate prediction of streaming media using network traffic prediction methods. Also bandwidth estimation is carried out for the wireless network which plays a significant role in predicting the session rates. Our proposed session rate expression helps to understand the significance of predicting the session rate for streaming media in mobile wireless network. We study various proposals in this regard and the state-of-the-art analytical analysis is presented followed by our notations.

A Wireless Body Area Network (WBAN) is considered as a special type of a Wireless Sensor Network (WSN) with its own challenges and requirements. A traditional WSN is designed to achieve higher efficiency, however, WBAN is designed to achieve maximum throughput, minimum delay and to maximize the energy efficiency, all together. Our analysis indicates that available protocols for WSN are not easily portable for WBAN, specifically Medium Access Control (MAC) Protocols. Thus, we have proposed a new WBAN MAC Protocol based on Aloha protocol which differentiates and preferred various types of traffic based on emergency case. The proposed protocol has been simulated using different scenarios and the obtained results are analyzed and compared using NS-2 Simulator.

Nowadays there is need to share the knowledge and collaboration with group of people with same interest because people and information which they want to access both is geographically distributed. Information technology plays a major role in the future of education, as they break geographical barriers and make educational content widely available. Knowledge collaboration is possible by creating virtual classrooms by interconnecting lecturers to geographically scattered students. Making educational material, such as tutorials and recorded lectures, available worldwide through high-storage infrastructures. Digitalizing and making books available through high-storage infrastructures. But this E-learning system requires huge investment for high-storage infrastructure. Some educational organizations and research institutes cannot afford expensive investments. On the other hand, in most of the organizations, there are large amounts of underutilized computing power and storage existing. Data Grid connects underutilized secondary storage of machines and provides huge secondary storage. It enables resource sharing among the geographically distributed users and resources. Therefore the institutes which do not have sufficient budget can make use of Data Grid efficiently. In this paper we have presented that how Grid computing can be used for inexpensive collaborative E-learning. Data replication increases availability, reliability of data and allows fast access to data. Replication reduces bandwidth consumption and congestion over communication line. Therefore replication is the major issue in Data Grid. Replication strategies determine when and where to create a replica of the data. A good replication strategy should ideally minimize file access time, reduce network congestion and minimize job execution time to improve performance of Data Grid. We strongly believe that the technological revolution that the grid is about to bring will definitely change the way that people deal with information and, ultimately, knowledge. In this sense, grid computing and e-learning form a perfect match.

Universal Mobile Telecommunications System (UMTS), as a core network can support both circuit-switch as well as packet-switch networks. UMTS supports IP Multimedia services by including the IP Multimedia Subsystem (IMS) as part of its core network. IMS services are used by authenticating user equipment with the UMTS using UMTS AKA and then with the IMS using IMS AKA. This paper concentrates on the IMS authentication and proposes an improved one-pass IMS authentication procedure which will result in reduction in message delivery cost and improvement over IMS AKA. This proposed procedure also preserves security features such as mutual authentication and key agreement expected from an IMS authentication procedure.

Grid computing provides the effective sharing of computational and storage resources among geographically distributed users. In most of the organizations, there are large amounts of underutilized computing power and storage existing. On the other hand most desktop machines are busy less than 5 percent of the time. Grid computing provides a framework for exploiting these underutilized resources and thus increases the efficiency of resource usage. Now a days, many commercial, business and research institutes produce huge amount of data and need to store this data on secondary storage of machines. Users of data are distributed among different geographical boundaries and they want to collaborate on the same problem. Data grids focus on providing secure access to distributed, heterogeneous pools of data. Data grids harness data, storage, and network resources located in distinct administrative domains, and provide high speed and reliable access to data. Optimization of data access can be achieved via data replication, whereby identical copies of data are generated and stored at various sites. A good replication strategy should ideally minimize latencies; reduce access time while optimizing resources. Hence in this paper we have focused on improving data grid performance. We have first presented a detailed analysis of various replication strategies like No replication, Always replication and the Economic model simulated by OptorSim. Next we have proposed a dynamic replication strategy which switches between No replication and Always replication based on file size and type of access. We argue that the proposed file size and type of access based replication algorithm will minimize the access latencies and execution time of jobs on Grid. In the next phase we shall proceed with implementing algorithm on simulator and observing the performance of the implemented algorithm.

In adhoc networks nodes change their position consistently, and routes are rapidly disturbed, thereby generating route errors and new route discoveries. AOMDV protocol computes multiple loop-free and link-disjoint paths but nodes are unaware of relative movement and location. In this paper a scheme AOMDV-APLP is implemented that makes AOMDV aware of accessibility of neighbor nodes in the network. Nodes acquire the accessibility information of other nodes through routine routing operations and maintain it in their routing table. Based on this information route discovery is restricted to only "accessible" and "start" nodes. Multiple paths are generated by accessibility prediction from which route with the strongest signal strength is selected depending on Link life value predicted by Link Breakage prediction technique. Simulation results show that using accessibility and link life knowledge in route discovery process results in reduction of MAC overhead, routing overhead and average end-to-end delay and improves the Packet delivery ratio to a large extent as compared to standard AOMDV which reflects effective use of network resources. The simulation is done using Ns-2.34 on Fedora 9 Linux environment.

The process of the alternative functioning of the mobile phones shall be detailed keeping in mind that these devices have less memory and less computing capacity. A simple background subtraction algorithm process may be used, that projects a moving object in consideration to be in the current view, when the subtraction of the current captured image and the background reaches a certain threshold. We suggest identifying the type of the object considering its shadow and then calculate the length of the shadow to decide and decipher type of object. The mobile camera enabled system can also count the total number of vehicles, even occluded vehicles to some extent is also obtained. Information may be processed on the mobile phone and immediate alert message be sent to the central processing unit.