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Über dieses Buch

This book gathers selected high-quality papers presented at the International Conference on Computing, Power and Communication Technologies 2019 (GUCON 2019), organized by Galgotias University, India, in September 2019. The content is divided into three sections – data mining and big data analysis, communication technologies, and cloud computing and computer networks. In-depth discussions of various issues within these broad areas provide an intriguing and insightful reference guide for researchers, engineers and students alike.



An ECC with Probable Secure and Efficient Approach on Noncommutative Cryptography

An Elliptic Curve Cryptography (ECC) is used on the Noncommutative Cryptographic (NCC) principles. The security and strengths of the manuscript are resilient on these two cryptographic assumptions. The claims on the Noncommutative cryptographic scheme on monomials generated elements is considered be based on hidden subgroup or subfield problems that strengthen this manuscript, where original assumptions are hidden and its equivalents semiring takes part in the computation process. In relation to the same, the research gap is well designed on Dihedral orders of 6 and 8, but our contributions are in security- and length-based attacks enhancement over Dihedral order 12, reported in work done. We modeled the said strategies and represent the ideal security concerns for applications.
Gautam Kumar, Hemraj Saini

Probability Prediction Using Improved Method in Delay-Tolerant Network

Delay-Tolerant Network is a widely used network these days. Nodes are connected with each other and pass information between each other. The node that obtains the message accumulates it and advances its duplicate to another node it experiences. There are many parameters used to evaluate the Delivery Probability (DP) and storage of each node is also used because in DTN all the nodes should be capable of storing the message if the path is not available. In this paper, an improved method (EEA-PRoPHET) is proposed for increasing the probability prediction and transitivity of the nodes.
Pradeep Yadav, Manuj Mishra, C. P. Bhargava

Taxonomy of Cyberbullying Detection and Prediction Techniques in Online Social Networks

Online social networking sites have become very popular in this era due to easy accessibility of Internet. This popularity leads to continuous availability of multiple users, which resultantly attract more criminals and hence increasing insecurity in OSN. Different types of crimes are committed for multiple reasons in cyber realm by taking assistance of cyber technology. This insecure environment of OSN needs attention to prevent the damage caused by these crimes to society. Cyberbullying is reported as one of the harmful crimes causing psychological damage to victims. Cyberbullying has dangerous effects on the victim, which may also lead the victim to suicidal attempt. Victims of cyberbullying are usually afraid or embarrassed to reveal about their harassment. It has become a necessity to detect and prevent cyberbullying. Many researchers are working in multiple directions to achieve best results for automated cyberbullying detection. We have done a broad survey of all recent techniques proposed by researchers for cyberbullying detection and prediction. In the paper, we have presented taxonomy of multiple methods being used for cyberbullying detection. We also have presented a comparative analysis and classification of the work done in recent years.
Madhura Vyawahare, Madhumita Chatterjee

A Formal Modeling Approach for QOS in MQTT Protocol

With the rising demand for IoT devices, communication protocols like MQTT, CoAP, and many more, have become an integral part of the system to ensure safe and reliable data transfer. Using lightweight communication protocols such as the Message Queuing Telemetry Transport (MQTT) protocol makes it much easier to establish communication between distributed devices as it easily recovers from connectivity loss, component failures, and loss of packets. The pivotal contribution of this paper is the method of approach to formally model, analyze, and verify the Quality of Service (QoS) levels of the MQTT protocol. A complete analysis of the Quality of Service levels is performed to confirm that it behaves correctly as specified when used in communication between different components. Formal modeling has been done using PROMELA language and the model verification is done using a system verification tool called SPIN Model Checker.
E. Archana, Akshay Rajeev, Aby Kuruvila, Revathi Narayankutty, Jinesh M. Kannimoola

Prediction of Gene Selection Features Using Improved Multi-objective Spotted Hyena Optimization Algorithm

Microarray data analysis is one of the main research areas in the medical research. The Microarray is a dataset which consists of different gene expressions from which most of the features are redundant genes and reducing the classifier accuracy. Finding a minimal subset of features from large gene expression is a challenging task where removing redundant feature but the important feature will not be missed. Many optimization techniques are introduced by the researchers to find a minimal subset of features but it does not provide a feasible solution. In this paper, the RWeka package, which provides an interface of Weka tool functionality to R is used to order the features using select attribute function in Weka. By using those ordered features, a minimal subset of features is selected using SVM classifier with maximum prediction accuracy in the dataset. Obtained minimal subset of features is given as input to the Multi-Objective Spotted Hyena Optimizer algorithm which is driven by the ensemble of SVM classifier by updating the search agents with objective function with an intension to improve the classification accuracy. The proposed method has experimented with seven publicly available microarray datasets such as CNS, colon, leukemia, lymphoma, lung, MLL, and SRBCT, which shows that the proposed methodology gives the high accuracy than all other existing techniques in terms of feature selection and prediction accuracy.
S. Divya, Eranki L. N. Kiran, Madhu Sudana Rao, Pujitha Vemulapati

A Compressive Family Based Efficient Trust Routing Protocol (C-FETRP) for Maximizing the Lifetime of WSN

WSN is deployed for the dissemination of various sensor nodes in a fixed topology to sense the environment with limited resources, and to communicate the sensed data with the base station through cluster head. WSN is one of the dynamic networks, which can perform several dynamic functions like change in cluster head, avoid redundant messages, resource reservation mechanism and resource cancellation mechanism. One of the major problems in the deployment of WSN is ‘terrain structure’. Due to irregular terrain structure, deployment of sensor nodes is random in nature and due to this random deployment; nodes are not properly organized in a distributed way. A Compressive Family-based Efficient Trust Routing Protocol is proposed by dividing the network into various clusters and then split up clusters into sub-clusters. Each sub-cluster is again separated into various families and each family is allocated with a family head. The performance of proposed approach is compared with similar protocols (GEED-M, EETRP, and FERP) developed for specific terrain structures like plateaus and military areas. The Compressive Family-based Efficient Trust Routing Protocol enhances the shelf life of the network by 69%, and reduces the energy consumption of the network by 30%.
Nandoori Srikanth, Muktyala Siva Ganga Prasad

An Adaptive Genetic Co-relation Node Optimization Routing for Wireless Sensor Network

Wireless sensor network is designed with low energy, and limited data rates. In wireless sensor networks, the sensors are designed with limited energy rates and bandwidth rates. Maximizing the network lifetime is a key aspect in traditional Wireless communication to maximize the data rate in typical environments. The clustering is an effective topology control approach to organize efficient communication in traditional sensor network models. However, the hierarchical-based clustering approach consumes more energy rates for large-scale networks for data distribution and data gathering process, the selection of efficient cluster and cluster heads (CH) play an import role to achieve the goal. In this paper, we proposed an Adaptive Genetic Co-relation Node Optimization for selecting an optimal number of clusters with cluster heads based on the node status or fitness level. Using the tradition Genetic Algorithm, we achieved the Cluster head selection and the co-relation approach identifies the optimal clusters heads in a network for data distribution. Cluster head election is an important parameter, which leads to energy minimization, and it is implemented by Genetic Algorithm. Appropriate GAs operators such as reproduction, crossover and mutation are developed and tested.
Nandoori Srikanth, Muktyala Siva Ganga Prasad

A Novel Hybrid User Authentication Scheme Using Cognitive Ambiguous Illusion Images

Text-based passwords are most common and easy to use but are difficult to memorize and remember. Moreover, they are prone to attacks like shoulder surfing and brute-force. On the other hand, graphical passwords are easy to remember and memorize. But they are still not commonly used as they have some issues like increased user login time, and small password space. In today’s scenario where number of data breaches is increasing, more secure authentication schemes are needed to ensure the authenticity of a user. In this paper, we propose a novel hybrid user authentication scheme by integrating both text-based and graphical password schemes to make authentication system stronger and resistant to attacks. Our scheme has two steps of authentication, in which at the first step, the user has to recognize and select his appropriate image among the blurred images and in the next step, the user has to enter the tag associated with the selected image. Only after successful completion of the two steps, the user is authenticated. The images used as a part of graphical password scheme are cognitive ambiguous illusion images. The basic idea behind using these images is that they are perceived by different users differently depending on how they visualize the image. To evaluate the effectiveness of the proposed scheme, an experiment was conducted on the setup and the results obtained were promising.
Sumaiya Dabeer, Mahira Ahmad, Mohammad Sarosh Umar, Muneeb Hasan Khan

Fault Classification in a Transmission Line Using Levenberg–Marquardt Algorithm Based Artificial Neural Network

The main objective of the power system is to supply reliable and quality electricity to all consumers. In this paper, the main focus of the author is to classify all types of faults, namely phase to ground, phase to phase, three-phase fault, and double line to ground faults that may occur at different fault locations and involve varying fault impedances in the power system using artificial neural networks (ANNs). Owing to the advantages of an artificial neural network to map nonlinearity in the data, to learn from examples and to generalize the pattern classification, ANN framework under supervised learning is implemented as a fault classifier. The proposed methodology includes extraction of features from phase voltages and currents obtained under normal and faulty conditions for different fault locations and fault impedances. The learning of feed forward ANN-based fault classifier is carried out using Levenberg–Marquardt algorithm for training the data obtained for IEEE 14 bus system.
Harkamaldeep Kaur, Manbir Kaur

IoT Botnet: The Largest Threat to the IoT Network

Adoption of the IoT technology is expanding exponentially. It is capable of providing a better service. IoT technology is successfully implemented on the bulb, refrigerator, air conditioner, washing machine, wristwatches, mobile phones, etc. Gartner report reflects that growth in the number of IoT devices is massive. By 2025, the number of IoT devices may reach up to 50 Billion. This growth poses an enormous range of challenges. The challenges are communication, interoperability, integration, data handling, privacy, and security. The major challenge is security. This paper focuses on different types of possible attacks on IoT and how the IoT botnet is gaining more attention and becoming a major attack. It highlights the key difference between traditional botnet and IoT botnet. Review of the existing techniques to deal with a botnet as well as the urge for a different technique to deal with IoT botnet is discussed.
Smita Dange, Madhumita Chatterjee

Building a Trustworthy Ethical Approach to Cloud Computing

Cloud computing has gained wide acceptance by business enterprises all over the globe. With the advancement in the popularity of cloud computing, research is being directed to study the effect of various aspects affecting it. One of the most important issues that impact cloud computing is the human perception of it. The inclination of users, across the globe, towards cloud computing is affected by the amount of trust they repose in the services and the perceived level of ethics being observed in maintaining these services. This paper studies the impact of ethics and trust on Cloud computing and its subsequent effect on e-commerce. A model is proposed to incorporate trust and ethics for an improved e-commerce environment (ETCM), Ethics Trust Cloud model. Further a survey is conducted to deduce the benefits of cloud computing on e-commerce where the respondent provides opinion about the prominent factors which contribute towards building of trust in the service provider and the possible relationship between observance of ethical practices and trust-building. The data is collected by a specific questionnaire, which is designed to understand the user’s perception about these concepts and their possible influence on cloud computing environment. Findings of the survey and subsequent analysis reveal a positive correlation and regression between trust and ethics.
Ankita Sharma, Hema Banati

Weighted Frequent Itemset Mining Using OWA on Uncertain Transactional Database

The technology of data mining has a broad scope in banking, manufacturing, medical science, and business decision-making. In these applications, the most commonly used term is Frequent Itemset Mining Algorithms which is one of the vital parts of Association Rule Mining. The evolutionary improvement in FIM is Weighted Frequent Itemset Mining, and these algorithms can be executed on Certain/Probabilistic Uncertain databases for calculating important frequent itemsets, having weight and expected support equal to or greater than user-specified minimum weight or minimum probability, respectively. The weight for an itemset is calculated by taking an average of weights of all items in the itemset. In this case, if the weights of one or two items are very high compared to others than the average can be decided by only higher weights and ignore low values. In this research paper, OWA operator is used in place of the traditional mean for calculating the weight of an itemset. A new algorithm is developed in this research and executed on example database. The results show that the generated itemsets are less in the count but hold more importance.
Samar Wazir, M. M. Sufyan Beg, Tanvir Ahmad

Design of Customer Information Management System

The Customer Information Management (CIM) needs to handle, maintain and store the customer data. To facilitate this objective, the need for an application that takes the customer and the item details from the external source files and store the same in the Data Warehouse (DW) is required. It is an ETL system developed to create the mapping to load the target from the source files after applying criteria as required by the administrator and will also provide the overall features and functionalities required to store and maintain customer data. The data stored in the DW is transformed (cleaned and integrated), thus it is credible and can be used for business insights. This serves as an important component for BI (Business Intelligence) which will help in transforming raw/operational data into some meaningful information. The Fact_Inventory which is developed provides us the historical data in a summarized form which can be used for managerial, strategic and analytical decisions by the Analysts team and the end users. It acts as a vast storehouse for the already operated data and can be referred to as an ‘Informational System’.
Rohini Narayan, Gitanjali Mehta

Modeling Machine Learning Agent for Interaction Conversational System Using Max Entropy Approach in Natural Language Processing

There are several service-oriented models, where services are deployed for the user. The user chooses any one of them. During the operational life cycle of these services, there are several issues that occur. User wants an interface for complaint. This paper uses the sentence boundary detection, NER, document categorization, and sentiment extraction methodologies. The natural language processing generates the training model which is a statistical representation of current system knowledge. When a user enters the input then training model extracts the value of a different parameter, these parameters used by call center model for better understanding of user input. The machine learning model used to generate a logical response. When the time exceeds the size of sample, data should be increased and the model understanding also more and more accurate. The accuracy of the model depends on the size of the sample training data. When training data size increases the statistical model for the call center is updated. When the user interacts with the call center agent then call center agent to extract the value of all parameters based on the current statistical model which is based on the sample training data. The paper uses different parameter such as NER, Document category, and sentiment for making a better user interaction. The probability of correct response is increase n time if n parameters are used for response generation. Call center module to take help from sentence detection, NER, document categorization and sentiment training model for extraction the value of the parameter. These parameter value helpful for extracting the NLP Text meaning. The response correctness also increases whenever anyone parameter is extracted correctly. The maximum entropy approach is used for making statistical modeling. The training data are taken from the heterogeneous source.
Anil Kumar Negi, Syed Imtiyaz Hassan

Analysis of Energy Consumption in Dynamic Mobile Ad Hoc Networks

Energy is a very crucial parameter in Mobile Ad Hoc Networks since mobile nodes are operated with this scarce resource. If nodes battery is drained, then ongoing transmission in the network is disrupted and discontinued. Mobile nodes can communicate with each other directly or multi-hop fashion. A mobile node uses battery power while working in various modes, i.e., transmitting, receiving, idle, and sleep. Routing protocols, MAC layer, and other network layers exchanges various control packets for executing their task. Overall, performance of the network indirectly depends on the battery power. So, wisely utilizing battery of individual nodes may significantly increase the network performance and lifetime. We have studied the energy utilization of individual node in the network. We have used AODV routing protocol for the analysis of individual node energy utilization in different mode of their operation. Finally, we observed that in receiving mode nodes average energy consumption is higher as compared to transmit mode operation.
Indrani Das, Rabindra Nath Shaw, Sanjoy Das

Improved ITCA Method to Mitigate Network-Layer Attack in MANET

MANET is well known for its inherent feature of on-demand ad hoc establishment of network. This makes MANET a suitable option for many applications like Disaster management, military applications, etc. But the mutual dependency among the nodes make the MANET vulnerable for many attacks. Researchers had proposed many solutions to make the routing in MANET secure, ITCA is one of the proposed examples which tries to identify malicious activity and isolate infected nodes from network through multiple dimensions. The Improved ITCA proposed in this paper tries to make attack detection system real time and trust calculation adaptive to the application-specific parameters. This will reduce the burden over source node, which has been used by most of ACK-based solution for attack identification and isolation, in turn tries to reduce the number of control packet required that optimize the overhead and make the attack detection and isolation process more simple and faster. The Improved ITCA introduces a lightweight real-time option for secured ACK-based approach, whereas adaptive application-specific trust calculation parameters make the system more robust or work efficiently even when percentage of malicious node in the network is high.
Nilesh R. Marathe, Subhash K. Shinde

Employing Machine Learning Models to Solve Uniform Random 3-SAT

We have employed a chosen set of machine learning models to solve the 3-CNF-SAT problem. Through f1-scores, we obtain how these algorithms perform at solving the problem as a classification task. The implication of this endeavour is exciting given the property of the NP-complete class problems being polynomial-time reducible to each other.
Aditya Atkari, Nishant Dhargalkar, Hemali Angne

A Design and an Implementation of Forecast Sentence Extractor

Strategic planning is a practical approach for researchers to conduct the STEEP analysis. One of the most promising approaches for strategic planning is the Foresight Framework. In the very first steps of Foresight Framework, however, the environmental scanning is involved. This process is time-consumed since a very large amount of data must be explored. To alleviate the time in such a process, this study proposes a design and an implementation of the forecast sentence extractor by using natural language processing and machine learning algorithm. The proposed algorithm digests a long article and then provides a short list of forecast sentences. Three feature selection approaches are tested. From the experimental studies, the accuracy of the proposed algorithm is up to 85.10%.
Benyatip Srichareon, Suparerk Manitpornsut, Prapas Pongdamrong

Low Complexity Antenna Selection Scheme for Spatially Correlated Multiple Antenna Cognitive Radios

In the spectrum sharing cognitive radio networks, spectrum sensing using multi-antenna can improve sensing performance by exploiting spatial diversity. But because of multipath fading, the spatial correlation arises which depends on the antenna spacing and angle of arrival. This spatial correlation degrades the sensing performance significantly. In this paper, a low complexity antenna selection scheme has been proposed to reduce the effect of spatial correlation and enhance the system sensing performance. In the proposed scheme, all possible less correlated antenna combinations are identified. The total sensing sub-slots are divided by the antenna combinations considered for sensing. In one sensing sub-slot one combination is being used. The theoretical results are verified with the simulations. The simulation results show that the proposed scheme outperforms conventional antenna selection schemes and detection probability approaches unity.
Sonali Chouhan, Tinamoni Taye

Fair Comparative Analysis of Opportunistic Routing Protocols: An Empirical Study

Fair comparative analysis of opportunistic routing protocols plays a vital role in selecting a suitable routing protocol for various applications of opportunistic networks. In this paper, we have analyzed the performance of the routing protocols, namely, EPIDEMIC, Spray and Wait, PROPHET, First Contact, Direct Delivery, MaxProp, WaveRouter, and LifeRouter. The ONE simulator is used for this empirical study. This study measures the performance of protocols based on Delivery Probability, Overhead Ratio, and Average Latency with different mobility models as well as real-world mobility traces. The simulation results surprisingly show that Spray and Wait outperform all the other protocols in almost all scenarios. Further, CAHM mobility model is able to mimic real-world mobility closely resembling real-world mobility traces of different network densities.
Jay Gandhi, Zunnun Narmawala

Distributed Optimal Power Allocation Using Game Theory in Underlay Cognitive Radios

In underlay cognitive radio networks (CRNs), primary licensed user and secondary unlicensed users use the same spectrum simultaneously by adjusting transmit power of secondary user. Such CRN consists of many secondary base stations (sec-BSs), primary base stations (prim-BSs), secondary user terminals (sec-UTs), and primary user terminals (prim-UTs). In this case, the major concern is to limit the interference at each prim-UT. This concern becomes a constraint for the sec-BSs in assigning transmit powers. In this paper, we develop the complication of power allocation to the sec-BSs as a concave game where there is no cooperation and communication between sec-BSs. The sec-BSs are considered to be players and the interference constraints are imposed by the prim-UTs. Unlike using the traditional Nash Equilibrium for equilibrium selection, we use the Normalized Nash Equilibrium, which is found by solving the necessary KKT conditions and checking the existence of the Lagrangian multipliers. The problem is further improvised by considering the battery leakage. The simulation results demonstrate the optimal power allocation for the sec-BSs taking the battery leakage into account.
Bhukya Venkatesh, Nadella Bala Sai Krishna, Sonali Chouhan

Relay Selection-Based Physical-Layer Security Enhancement in Cooperative Wireless Network

Broadcast nature during the data propagation and wireless transmission from the source to destination node can be easily overheard by the unauthorised users due to security issues. It cause interception and is highly vulnerable to eavesdropping effect. In this paper, a hybrid algorithm is proposed to overcome the limitations in physical-layer security and achieve optimal local solution. Cooperative-based relay selection approach is proposed to enhance the network range and durability in wireless communication and double threshold-based relay selection scheme to improve the spectral efficiency and overall quality of the communication system. Furthermore, the hybrid evaluation algorithm enhances the performance parameters such as signal strength and channel capacity; also it minimises the number of nodes. The proposed relay selection scheme is compared with direct transmission, P-AFbORS, P-DFbORS schemes and it is observed that better results are achieved from the proposed multi-relay selection scheme as compared to other the existing relay selection schemes.
Shamganth Kumarapandian, Martin James Sibley

Analysis of Performance of FSO Link During the Months of Monsoon in Delhi, India

The FSO communication links use an open free space for transmission and thus must be designed to withstand the atmospheric challenges which affect the capacity of the system. Rain is one of the foreign elements which can deteriorate the performance of the link and cause huge effect on the reception of the signal. In this paper, we present a comprehensive survey of attenuation due to rain conditions in FSO link in the months of monsoon in Delhi region. The rain attenuation used for simulation of the system has been calculated by using Marshal and Palmer rain distribution model for four specific months of the rainfall, i.e., June to September. Q-factor of the received signal has been analyzed by varying the transmission wavelength, data rate, and range of the FSO system. The simulation results show that for error-free transmission of data during the months of rainfall, the transmission signal wavelength in the longer wavelength region at a wavelength around 1550 nm is recommended to be used for a maximum data rate of 2.5 Gb/s with a transmission range of 4 km.
Sanmukh Kaur, Syed Zafar Ali Raza, Jaideep Khanna, Anuranjana

Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm

Breast Cancer is the most habitually detected neoplasm amid women in India and it is one of the principal reasons for cancer decreases in females. In order to visualize the breast cancer, radiologists prefer to use mammogram. It consists of many artifacts, which negatively influences the detection of breast cancer. Presence of pectoral muscles makes abnormality detection a cumbersome task. The recognition of glandular tissue in mammograms is imperative in evaluating asymmetry between left and right breasts and in guesstimating the radiation risk connected with screening. Thus, the proposed technique focuses on breast part extraction, muscle part removal, enhancement of mammogram, and segmentation of mammogram images into regions conforming to different densities. The anticipated method has been verified on Mini-MIAS database mammogram images with ground truth offered by expert radiologists. The results show that the proposed technique is efficient in removing pectoral muscles and segmenting different mammographic densities.
Jyoti Dabass


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