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This study employs the Fuzzy Analytic Hierarchy Process (FAHP) to examine and rank factors contributing to the self-perceived employability of final-year computer science students. Focusing on cognitive skills (SP-CF), non-cognitive skills (SP-NC), and emotional quotient (SP-EQ), each category is further dissected into sub-factors. A questionnaire was administered to 105 experts, using a nine-point Likert scale to rate the importance of these factors and sub-factors. FAHP was leveraged to quantify these aspects, taking into account the subjectivity and vagueness inherent in human decision-making. The study found that emotional quotient holds the highest weightage (0.5494), followed by non-cognitive skills (0.3236), and cognitive skills (0.1270), in affecting self-perceived employability. Within these categories, adaptability in non-cognitive skills and self-management in emotional quotient were particularly impactful. Minimum Degrees of Possibility (MinDy) were also calculated, with emotional quotient scoring the highest (1.00), indicating its overwhelming significance. Cognitive skills had the lowest MinDy (0.23), suggesting they are the least reliable factors in determining employability. These findings emphasize the importance of emotional intelligence over cognitive skills for employability, aligning with the growing academic discourse. The research offers valuable insights for educational institutions, helping them understand which attributes to focus on for enhancing the employability of their graduates.

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Wireless communication has become increasingly popular in the past two decades. The purpose of 5G is to provide higher bandwidth, lower latency, greater capacity and enhanced QoS (quality of service) than 4G. The 5G cellular network combines two technologies, SDN (software-defined network) and NFV (network function virtualization), for advanced management of the Network. This paper presents the main concepts related to RA (resource allocation) in a 5G network, which is the idea of dividing the network into multiple independent networks, each satisfying specific requirements while offering superior QoS. 5G network services can be classified into three verticals – (i). enhanced-Mobile Broadband (e-MBB), (ii). ultra-Reliable and Low Latency Communication (u-RLLC), and (iii). Massive-Machine Type Communications (m-MTC). Users require well-organized resource allocation and management. In this work, we implement a resource allocation module with Deep Reinforcement Learning (DRL) to estimate the Q-value function that utilizes a deep neural network, which learns from previous experience and adjusts to changing environments. The outcomes demonstrate that the implemented simulation reaches better in resource allocation compared to previous models, leading to lower latency and better throughput.

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A wireless sensor network (WSN) comprises multiple sensor nodes that establish communication among themselves to share data with a central base station. Such networks find utility in various small-scale applications, enabling the creation of a MAN-less network for efficient data collection. Data interchange between sensor nodes and the base station relies on hierarchical routing protocols. The network supports both stationary and moving base stations, employing a topology consisting of multiple clusters. Each node within a cluster communicates with the respective cluster head, which, in turn, communicates with the base station. The proposed approach involves the utilization of a hierarchical routing protocol enhanced by a Genetic technique and incorporates a moving base station. This approach has shown significant improvements in performance parameters such as the count of inactive nodes, remaining energy levels, and overall node vitality.

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This study aims to develop an empirical model to predict the employability of computer science graduates considering cognitive, non-cognitive traits and emotional quotient, and their interplay with academic performance. Design/methodology/approach: A multistage sampling technique was employed on final-year computer science students across various universities in the country. Structural Equation Modelling was used to test the hypotheses and analyze the data collected from 562 respondents. Findings: The findings indicate that cognitive abilities (specifically problem-solving and decision-making skills, knowledge of science and engineering principles, knowledge of contemporary issues, and competency in specific engineering disciplines), non-cognitive traits (extraversion, conscientiousness, agreeableness, openness to experience, and a negative impact from neuroticism) and emotional quotient (intrapersonal and interpersonal skills, adaptability, and stress management) significantly predict self-perceived employability. However, competency skills and engineering system approach were not found to have a significant impact. The model accounted for 48.57% of the variation in self-perceived employability. Research limitations/implications: The study was limited to computer science students in one country. Future research should consider other disciplines and countries to generalize the findings. Practical implications: The results underscore the need for the integration of these skills in the curriculum and pedagogical approaches of computer science programs to enhance the employability of graduates. Originality/value: This study extends the existing literature by developing an integrative model that incorporates cognitive, non-cognitive, and emotional quotient abilities to predict the self-perceived employability of computer science graduates.

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Today customers are highly exposed to the essential amount of information related to products and services like never before. This leads to an enormous amount of diversity leading to consumer’s demand, thus becoming a challenge for the retailers to cater right products and services as per customer’s preferences. Customer reviews, opinions and shared experiences related to a product turns to be an effective source of information about customer’s preferences that can be utilized by recommender systems. To recommend products to the users, buying list of users, viewer’s list and purchase count of the products are considered as main fundamental attributes for conducting the analysis of the products purchased and viewed. In this paper, a hybrid recommendation model that combines machine learning, collaborative filtering and data analytic is proposed. The recommendation algorithm begins to acquire cluster of similar users. Further, Similar shopping basket of customers is prepared with User-item matrix and purchase count of the products. The data set is then clustered as per the requirements to form train-test data. The Experimental Results shows low error i.e. lower root mean square and has high precision and high response time in comparison to Popularity based (Baseline) recommendation model.

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Customer Relationship Management systems have been used to allow businesses to attract new customers, develop a long - term relationship with them and improve the retention of customers for greater profitability. CRM systems use machine-learning models to evaluate personal and behavioural data of customers to give company a competitive edge by increasing the retention rate of customers. These models can predict customer’s purchases, and their reasons. Predictions are used in the development of targeted marketing plans and service of erings. This paper focuses to develop a framework by applying machine learning techniques to predict the purchases by the customers on e-commerce platform. Through clickstream and additional customer data, frameworks for predicting customer behaviour can indeed be developed. Predicting potential consumer behaviour generates pertinent information for sales and marketing teams to strategically focus on dif erent resources. Such information facilitates inventory planning at the warehouse and at the point of sale, as well as strategic decisions during production processes can be planned accordingly. Next, this research provides insight into the performance dif erences of the models on sequential clickstream and static customer data by performing a data analysis and training the models separately on the various datasets. Deep Belief network along with auto encoders and Adam optimizer is performed to validate the data and predict the likelihood of purchase and customer conversion probability.

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The renal cancer is very serious and rapidly growing disease. It is very necessary that the abnormalities in renal or kidney of a patient will be identified so that cancer will not reach to its advanced stage or phase. Therefore, the early detection of renal cancer is very important to save the life and to increase the life span of particular patient. In this study, an intelligent system using adaptive neuro-fuzzy inference system (ANFIS) is proposed to diagnose renal cancer. This proposed system helps to classify the various stages of renal cancer such as no cancer, stage 1, stage 2, stage 3 or stage 4 cancer. Hence, the prime objective of this study is to diagnose renal cancer by using adaptive neuro-fuzzy inference system (ANFIS). There are seven input variables that are given to the system. These are haematuria (blood in urine), red blood cell count, flank pain, tumour size, Von Hippel-Lindau gene, high blood pressure and trichloroethylene exposure. Similarly, the output corresponding to given inputs is stage of renal cancer. The output can be no cancer, stage 1, stage 2, stage 3 or stage 4 of renal cancer. The MATLAB software is used to implement this adaptive neuro-fuzzy inference system (ANFIS). The developed medical intelligent system for the detection of renal cancer shows the correct and accurate results with accuracy 96%.

Abstract

In this research work, a new multilayer fuzzy inference system is proposed for diagnosis of renal cancer. This proposed automated diagnosis of renal cancer using multilayer Mamdani fuzzy inference system can help to classify the different stages of renal cancer such as no cancer, stage 1, stage 2, stage 3 or stage 4 cancer. This expert system has four input variables at layer 1 and similarly seven input variables at layer 2. At layer 1, the input variables are smoking, dialysis, occupational exposure and genetic or hereditary that recognize the output conditions of renal or kidney to be normal or to have renal cancer. The further input variables for layer 2 are haematuria (blood in urine), red blood cell count, flank pain, tumor size, Von Hippel-Lindau gene, high blood pressure and trichloroethylene exposure that reveal the output condition of kidney such as stage 1 cancer, stage 2 cancer, stage 3 cancer or stage 4 cancer. The novelty in this research work is development of multilayer fuzzy inference system that deals with fuzzy values, uncertain and ambiguous data to detect the stage of renal cancer by using two layers. This paper presents an analysis of results accurately using the proposed expert system to model the renal cancer process with medical expert advice. The confidence indicator for this proposed expert system is 95%.

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Renal cancer is a serious and common type of cancer affecting old ages. The growth of such type of cancer can be stopped by detecting it before it reaches advanced or end-stage. Hence, renal cancer must be identified and diagnosed in the initial stages. In this research paper, an intelligent medical diagnostic system to diagnose renal cancer is developed by using fuzzy and neuro-fuzzy techniques. Essentially, for a fuzzy inference system, two layers are used. The first layer gives the output about whether the patient is having renal cancer or not. Similarly, the second layer detects the current stage of suffering patients. While in the development of a medical diagnostic system by using a neuro-fuzzy technique, the Gaussian membership functions are used for all the input variables considered for the diagnosis. In this paper, the comparison between the performance of developed systems has been done by taking some suitable parameters. The results obtained from this comparison study show that the intelligent medical system developed by using a neuro-fuzzy model gives the more precise and accurate results than existing systems.

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Background and objective: The most prevalent type of kidney cancer is renal cell cancer which typically takes place at advanced or final stages of cancer. Renal cell cancer spreads rapidly and can be fatal if the disease is not spotted in the early stages. Hence, it is crucial to recognize kidney deformities before it is in its final phase. However, in this context, only a few review articles have been published almost 12 years ago. Hence, a systematic review was conducted to determine work done by various researchers on kidney cancer and to identify the research gaps between the studies so far. Methods: To accomplish a structured review, all the relevant papers were categorized based on the authors' names, year of publication, main objective of the research, system input and output variables and finally remarks which gave erudite information apropos the research which has been reported in various publications. After that, the numerous conclusions regarding diagnosing kidney cancer have been scrutinized. Result: The outcome of this study permitted the effective diagnosis of kidney cancer or renal cancer to be done using the adaptive neuro fuzzy method with a 94% accuracy. Although, many data mining techniques were applied by authors, the accuracy of these methods was lower than the adaptive neuro fuzzy method. This method is useful to identify the aspects of diagnosis of renal cancer more rigorously and ameliorate. Conclusion: Overall, an appropriate platform is described by this chapter to detect the research gaps in the field to analyze kidney or renal cancer for further studies or researches.

Abstract

The increasing growth of the renal cancer became the most crucial issue in the society. Renal cancer has to be predicted at its introductory stages to prevent the last or end stages. The early identification and treatment will keep the renal cancer from getting worse. There is no any awareness about its growth, its end effects and if it increased then how can it affects the health of a patient. Hence, there is requirement of advanced diagnostic system which assist to maintain the health of an individual. The main goal of this undertaken research is to merge the developed data reduction techniques and the supervised classification technique. The data reduction technique used in this research work for the detection of renal cancer is Principal Component Analysis (PCA) and similarly, the used supervised classification technique id K- Nearest Neighbour (KNN). The examination of the dataset as well as the observed result is done by considering several performance parameters such as classification accuracy, sensitivity, specificity and precision. The result deduced that the Principal Component Analysis (PCA) with K-Nearest Neighbour (KNN) has the better results with classification accuracy 93.33%.

Abstract

The renal cancer is a common type of cancer in the old ages and the advancement of renal caner is so rapid that it should be detected at initial stages. In this research work, a multi-layered fuzzy Sugeno model has been developed to diagnose the renal cancer. For the first layer, there are four input variables such as Smoking, Dialysis, Occupational exposure and Genetic or hereditary that detects whether the patient of given data is suffering from renal cancer or not. Hence, the output of first layer is cancer present or no cancer. In the second layer, the seven inputs are taken to decide the stage of renal cancer by the system. These input variables are Haematuria, Red blood cell count, Flank pain, Tumor size, Von Hippel-Lindau gene, High blood pressure and Trichloroethylene exposure. The proposed system helps to classify the different stages of renal cancer in the correct classes. These stages such as stage 1, stage 2, stage 3 and stage 4 of renal cancer are considered as output of the system or the classes in which the given input should be classify. The MATLAB software is used for the development of fuzzy Sugeno model to diagnose the renal cancer. The proposed system has been evaluated on the basis of parameters of performance. The considered performance parameters are classification accuracy, specificity, sensitivity and precision. According to the evaluated result, the developed fuzzy Sugeno model has 96.5% classification accuracy.

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The ever increasing demand of data traffic due to emerging services and applications has evolved the need of high capacity, high speed and highly spectral efficient optical systems with advanced modulation formats. We compare the performance of single channel coherent optical system for 112 Gb/s with Dual polarization modulation techniques i.e. DP- QPSK(Dual Polarization Quadrature phase shift keying), DP-16QAM(Dual Polarization −16 Quadrature amplitude Modulation) and DP-32QAM(Dual Polarization −32 Quadrature amplitude Modulation) with DSP (Digital Signal Processing)module for SSMF(standard single mode fiber) in terms of tolerance to fiber nonlinearities which is investigated through the effect of power and distance on %EVM and requirement of OSNR using numerical simulations. ned shows that the coherent optical systems with higher order modulation formats are less tolerant to fiber nonlinearities i.e the performance of DP-QPSK coherent systems with DSP module is better than the DP-16QAM and DP-32QAM coherent systems as they are more tolerant to fiber nonlinearities. The results obtained can be used for the design of compensation techniques for fiber nonlinearities in cohResults obtaierent optical systems using higher order modulation formats.

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The abnormal and anomalous observations even in the advanced technological era proves to be the biggest jolt to the concerned industry. To reduce and eliminate the outliers from the massive data streams...

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Curse of Dimensionality and the attribute relevance is the matter of great concern now these days while dealing with the higher dimensional data sets or Big Data, especially to detect the projected outliers. The objective of this research paper is to construct a Robust and a scalable model to prominently highlight the higher dimensional outliers in an effective and an efficient manner. Methods/Analysis: In order to detect the projected outliers, an algorithm EKFFK-Means with a hybrid approach is constructed using two important methodologies- Extended Kalman Filter (EKF) and Fuzzy K-Means. EKF is used to linearize the higher dimensional data by estimating the current mean and covariance by enhancing the Kalman gain and then fuzzy K-Means confirms the outlying property of each data instance and categorizes them in an effective and an efficient way using the membership label. Findings: A model EKFFK-Means is constructed that further creates 30 clusters from the complete data set to detect the projected outliers and various parameters like accuracy, cluster validity, True positive rate, False positive rate , robustness and cluster quality are calculated. Improvements : This algorithm is further compared with HPStream and CLUStream and is proved better against various parameters.

Abstract

Region of interest detection in ultrasound image is a challenging task due to heterogeneous texture and presence of speckle noise. The ultrasound scanning is most frequently used tool to examine the patient for abnormities, especially presence of stone, in the kidney. Automatic object detection in ultrasound images is burning research areas and the present research work is in the same direction. We have developed an application, which helps the medical practitioner to identify the stone region in the ultrasound image. It is a semiautomatic system in which practitioner need to select the region, which is analyzed, by the proposed system for presence of stone. The feature extraction is applied on cropped regions, which may contain stone. The various features such as Contrast, Angular second moment, Entropy and Correlation are used. The KNN classifier is used to classification based on training image dataset. The overall accuracy of classification system is around 91%. The confusion matrix is also prepared to analyze the complexity and accuracy of the proposed system.

Abstract

Kidney ultrasound imaging is an economical, non invasive, real time diagnosis system which is used to measure abnormalities in the shape, size and location of the kidneys in the body. The presence of speckle noise degrades the visual quality of ultrasound images. The primary objective of the research under taken is to detect the renal stone in ultrasound images. The present research paper discusses an algorithm based on cellular automata and nonlocal mean for suppression of the speckle noise. The cellular automatons are dynamic systems to represent the particular problem in terms of specific pattern. This concept is exploited to distinguish the noise from the object being target. The kidney ultrasound images are denoised to detect presence of renal stone in the image.

Abstract

We study and compare the performance of 112Gb/s Dual polarization coherent optical system for single channel coherent system with different modulation techniques,DP- QPSK(Dual Polarization Quadrature phase shift keying), DP-16QAM(Dual Polarization 16 qadrature amplitude Modulation) using numerical simulations with inline DCF(dispersion compensation fiber) and without inline DCF( but with DSP module for dispersion compensation)using SSMF(standard single mode fiber) for the effect of fiber nonlinearities. Inline dispersion post compensation system was explored to mitigate the impact of transmission impairments. Results obtained shows that the performance of DP-QPSK coherent systems with inline dispersion post compensation is better than the electronic dispersion compensation using DSP module as with DCF they have more tolerance to fiber nonlinearities than those with electronic dispersion compensation but for DP-16QAM coherent system the performance with electronic dispersion compensation is better than with inline dispersion compensation.

Abstract

We have presented Digital Image watermarking for gray scale images which implemented in frequency domain. In this paper, a binary watermark pattern was constructed from the information content of the image by selecting the minimum value from every block of size and was disordered with the help of Arnold Transform. Peak signal to Noise Ratio and Normalized Cross-correlation are computed to measure image quality. Experimental result shows that the watermarking algorithm is robust against common signal processing attacks and this is also tested against multiple attacks and obtained result reveals the high effectiveness of the method.

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We investigated the performance of Raman-EDFA hybrid optical amplifier using SOA booster for 100 channels DWDM transmission system with each channel having data rate of 10 Gbps at reduced channel spacing of 0.2 nm.....

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Outliers are the data objects that do not conform to the normal behaviour and usually deviates from the remaining data objects may be due to some outlying property ....

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With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large amount of interesting data patterns and winnows the malicious data entering in any field of concern...

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We have been living in an era of rapid integration of IT technologies into business processes and people’s day to day lives. Large volumes of functional and non functional data is being produced everywhere which companies desire to make use of. Data mining techniques and data analytics have become essential in making useful sense of this sea of data for any business to prosper. Taking an example of retail sector, internet users in India have been becoming increasingly aware and receptive towards online retail. Noticing this traditional retailers have been focusing on increasing their online presence and adapting to new marketing strategies thereby utilizing this medium. In this paper we discuss in detail the facets of online retail, e shopping and changing needs of consumers also our research objectives and how we propose to identify the factors influential in affecting consumer perception and their satisfaction rate.

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Interconnection networks (IN) play very important role in various parallel processing applications. The connectivity pattern used in interconnection networks greatly determines the performance of the system. Multistage Interconnection Network is also an IN. The interconnection pattern used also determines the fault tolerance of the network. This paper introduces a new irregular fault tolerant multistage interconnection network named Irregular Modified Shuffle Network (IMSN). IMSN is analyzed in terms of fault tolerance and it has been observed that IMSN provides more alternate paths than existing networks such as Improved Four Tree Network (IFTN), Modified Alpha Network (MALN) and New Irregular Augmented Shuffle Network (NIASN).

Abstract

Ultrasound imaging can be used as an initial evaluation method in Urological diagnosis to examine kidney problems. The diagnosis helps doctor to access abnormalities in kidney like abnormality in size and shape of kidneys, presence of swelling, cysts, ulcer and stones. However, the Ultrasound diagnosis is more popular as compare to Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scanning, but it suffers with speckle noise. The speckle noise is an inherited property of ultrasound images which shows bright particles in the ultrasound images.

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In the present position of global networking, we want to protect our digital contents i.e. audios, videos, pictures and texts from unauthorized copying. For this reason, Digital Image watermarking Techniques have been developed to the copyright protection of digital data from illegal modification and reproduction. It is used in various areas like broadcast monitoring, copyright protection and also for owner identification. This paper highlights the basic model of Digital watermarking including its types and applications and also about various attacks on watermarked images. In this paper we also discussed about two methods i. e spatial domain and frequency domain.

Abstract

Inter-processors communication is one of the key issues of the system performance. The ability of networks to continue operating despite failures at minimum cost are major concerns in determining the overall system performance. In this paper, a new irregular multistage interconnection network is proposed named as Irregular Augmented Shuffle Exchange Network-4 (IASEN-4). The Performance of IASEN-4 is measured over some popular MINs in terms of permutation passable, fault-tolerance and cost effectiveness. It has been observed that IASEN-4 provides much better fault tolerance by providing more paths between any pair of source-destination at lesser cost as compared to other popular networks such as IASEN-3[12], IAON [8] and IASEN-2[11]. Key Words: Multistage Interconnection Networks, Irregular Augmented Shuffle Exchange Network, Irregular Advance Omega Network, Permutation Passable, Cost-Effectiveness.

Abstract

The Multistage Interconnection Networks (MINs) are basically a class of high-speed computer networks that usually consist of processing elements (PEs) at one end and memory elements (MEs) at the other end of the network. These PEs and MEs are connected by switching elements (SEs) which are themselves usually connected to each other in stages, hence the name. MINs are typically used as a low-latency interconnection as opposed to traditional packet switching networks because interconnecting processors and linking them efficiently to memory module is not an easy task. Hence, an interconnection network which can provide a desired connectivity and optimum performance is required. This paper presents a new type of such Irregular Multi-Stage Interconnection Network (MIN) named as Irregular Augmented Shuffle Exchange Network-4 (IASEN-4). The study of the existing regular and irregular multistage interconnection networks was carried out and their performance parameters were analyzed followed by the designing of IASEN-4 and computation of its respective performance factors. This proposed network is compared with the existing networks, viz. IASEN-3, IAON and IASEN-2 in terms of Bandwidth, Probability of Acceptance, Processor Utilization, Processing Power and Throughput. In this paper, we have proved that IASEN-4 is much better than the above-mentioned networks by comparing their aforesaid parameters.

Abstract

In this paper variety of fault tolerant multistage interconnection networks that have been proposed in literature are surveyed .Fault tolerant networks are capable of handling requests even in presence of faults although at reduced performance. Various networks which we have surveyed include Augmented Baseline Network (ABN) , Irregular Augmented Baseline Network (IABN), Modified Augmented Baseline Network (MABN), Augmented Shuffle Exchange Network (ASEN-2), Irregular Augmented Shuffle Exchange Network (IASEN), Irregular Augmented Shuffle Network (IASN),Improved Irregular Augmented Shuffle Network(IIASN), New Irregular Augmented Shuffle Network( NIASN), Modified Augmented Shuffle Exchange Network (MASEN), Irregular Augmented Shuffle Exchange Network–3 (IASEN-3), Modified Alpha Network (MALN), Advance Irregular Alpha Multistage Interconnection Network (AIAMIN2), Irregular Advance Omega Network (IAON).

Abstract

Data Mining simply refers to the extraction of very interesting patterns of the data from the massive data sets. Outlier detection is one of the important aspects of data mining which actually finds out the observations that are deviating from the common expected behavior...

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Wireless sensor network, IEEE 802.15.4/Zigbee is a specification for a suite of high level communication protocols that interconnects simple low power low processing capability wireless nodes for the transfer of data This paper aims at finding out the suitable mobility model that determines the effective positioning of nodes that gives the best throughput. In this study, the effect of four different mobility models, such as group, random walk, random waypoint and pursue mobility models has evaluated on various IEEE 802.15.4 based zigbee standard network. After the intensive simulations, it has been seen that by using the tree topology for the placement of nodes in the network and by configuring the movement of the nodes according to the group mobility model, network gives the maximum throughput and possess minimum data dropped in the network. So, it has concluded that under the given conditions, group mobility model outperforms other three mobility models.

Abstract

Fiber-optic communication systems have revolutionized the telecommunications industry. It has number of advantages over electrical transmission Recently, the interest lies in the increase of spectral efficiency as well as tolerance against dispersion effects and fiber nonlinearities which are today’s the most limiting factors in ultra long haul communication systems. With the new possibilities offered for the high speed digital circuits, coherent systems have attracted a lot of attention during the last years. Coherent detection is a promising technology to increase optical receiver sensitivity, enables supporting of more spectrally efficient modulation formats such as quadrature phase shift keying (QPSK) and quadrature amplitude modulation (QAM) and allows digital signal processing for compensation of transmission impairments.

Abstract

Data Mining is the process of extracting previously unknown but significant information from large databases. It is also termed as the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. Data and Information or Knowledge has a significant role on human activities. Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information. To analyze, manage and make a decision of such type of huge amount of data we need techniques called the data mining.

Abstract

Mobile ad hoc networks communicate without any fixed infrastructure or ant centralized domain. All the nodes are free to move randomly within the network and share information dynamically. To achieve an efficient routing various protocols have been developed so far which vary in their nature and have their own salient properties. In this paper, we have discussed one of the latest protocols i.e. Dynamic Manet on demand (DYMO) routing Protocol, implemented and analysed its performance with other similar protocols against different parameters. Finally a comparison has been presented between all of them.

Abstract

A mobile ad-hoc network (MANET) is basically called as a network without any central administration or fixed infrastructure. It consists of a number of mobile nodes that use to send data packets through a wireless medium. There is always a need of a good routing protocol in order to establish the connection between mobile nodes since they possess the property of dynamic changing topology. Further, in all the existing routing protocols, mobility of a node has always been one of the important characteristics in determining the overall performance of the ad hoc network. Thus, it is essential to know about various mobility models and their effect on the routing protocols. In this paper, we have made an attempt to compare different mobility models and provide an overview of their current research status. The main focus is on Random Mobility Models and Group Mobility Models. Firstly, we present a survey of the characteristics, drawbacks and research challenges of mobility modeling. At the last we present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Also, we illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.

Abstract

Piracy in the presence of internet and computers proves to be a biggest damage to the industry. Easy editing and copying of images yields a great damage to the owner as original images can be distributed through internet very easily. To reduce the piracy and duplicity of the digital multimedia files, digital watermarking technique is dominating over the other available techniques. There are certain methods or attacks which are used to damage the watermark. One of the major attacks is histogram equalization and reducing the number of histogram equalized levels. Thus, there is a need to develop a method so that the watermark can be protected after histogram equalization.

Abstract

In this paper, we discuss the basic routing technique of ants and study the change in pheromone values at each node. Also the optimal paths can then be computed based on the shortest cumulative pheromone count between source and destination nodes. AntNet is a distributed multi-agent system inspired by the stigmergy model of communication observed in ant colonies. The ants or control packets collect information about the network conditions and are used to update and maintain the routing tables. Ants based routing is gaining more popularity because of its adaptive and dynamic nature. A number of Swarm Intelligence based, more specially Ant Colony Optimization (ACO) based routing algorithms are proposed by researchers. A version of ant routing protocol called AntNet has been implemented to work within the network simulator ns-2. Routing tables and Pheromone tables have been computed for each node in the network. On the basis of these tables we have tried to compute the shortest and most optimal path between source node and destination node.

Abstract

Routing in an ad hoc network is always a challenging task because of the free and random movement of nodes. This high node mobility sometimes results in route failures and decreased network performance. Itfurther degrades the overall performance of the routing protocol. In this study we have analyzed the effect of two node mobility parameters, i.e. varying speed and number of nodes, on the throughput of Ad hoc on-demandDistance Vector Routing Protocol. The performance is experimentally tested and compared using ns2 simulator.The simulation results are graphically shown and explained.

Abstract

Apart from tremendous research being done all around the globe, still ad hoc networks are a big challenge for the researchers. Routing in an ad hoc network is extremely challenging because of its dynamic nature, limited bandwidth and power energy. Somehow, Swarm Intelligence based techniques such as ant colony optimization (ACO) algorithms have shown to be a good technique for developing routing algorithms for ad hoc networks. ACO based routing is an efficient routing scheme based on the behaviour of foraging ants. The collective behaviour of ants helps to find the shortest path from the nest to a food source, by deposition of a chemical substance called pheromone on the visited nodes. This mechanism from collective intelligence is applied to the ad hoc network by researchers. In this paper, we have brought some characteristics as well as performance analysis of the proposed ACO based ad hoc routing protocols and compare them with the well-known ad hoc routing protocols. The results presented in the last also help the researchers to understand the differences among various ACO based routing algorithms and to choose appropriate protocol for their research work. Our study shows how this approach has significantly improved the performance of the ad hoc networks.

Abstract

A number of routing protocols has been proposed in recent years for possible use of Mobile Ad Hoc Networks in various application areas such as military, govt. etc. In this paper we have presented a comprehensive review of these protocols with a particular focus on their security aspects. Further we have presented a comparison of some of the existing Routing Protocols of MANETs. The base criteria for comparison is routing methodologies and the information used to make routing decisions. All the protocols have to meet five security requirements: confidentiality, integrity, authentication, non-repudiation and availability, with respect to which the analyses of secure versions of proposed protocols are discussed.

Abstract

Proteins are the most important macromolecules of life. The knowledge of protein function is an important link for the development of drugs, crop development and synthetic bio-chemicals like bio fuels. Different techniques are applied for the protein function prediction. These techniques are based on protein sequence, protein structure, genomic data, protein interaction networks etc. In this paper we will use docking for the prediction of protein function .The docking technique makes the database search faster .By applying docking for the protein function prediction we can easily find the complementary proteins for the target proteins during the drug discovery process which makes this process fast. . Docking technique also helps in classification of enzymes and their mechanisms .By applying docking the virtual screening of the compounds could be done.

Abstract

In recent years, a vast research has been seen going on in the field of Mobile Ad Hoc Networks (MANETs). Due to limited resources in MANETs, to design an efficient and reliable routing strategy is still a challenge. An intelligent routing strategy is required to efficiently use the limited resources. Also the algorithms designed for traditional wired networks such as link-state or distance vector, does not scale well in wireless environment. Routing in MANETs is a challenging task and has received a tremendous amount of attention from researchers around the world. To overcome this problem a number of routing protocols have been developed and the number is still increasing day by day. It is quite difficult to determine which protocols may perform well under a number of different network scenarios such as network size and topology etc. In this paper we provide an overview of a wide range of the existing routing protocols with a particular focus on their characteristics and functionality. Also, the comparison is provided based on the routing methodologies and information used to make routing decisions. The performance of all the routing protocols is also discussed. Further this study will help the researchers to get an overview of the existing protocols and suggest which protocols may perform better with respect to varying network scenarios.

Abstract

The corporate LAN has evolved from a passive background business component to a highly active, highly visible core asset that enterprises rely on to support day-to-day operations critical to their market success. Today’s network is a strategic instrument that must be accessible anytime from anywhere—simultaneously offering fast, secure, reliable services at scale regardless of location. It has also evolved from traditional client/server data flow support to peer-to-peer flow support, and it must also accommodate an increasing number of devices and services. The main aim of this research paper is to demonstrate the need for implementation of switches in the design of LANs using OPNET-MODELER. The performance of a 16-station LAN using first a simple hub, and then a switch and two hubs is compared. By analyzing the graphs, it is concluded that traffic performance of a network after deploying switches is better.

Abstract

The field of Mobile Ad hoc Networks (MANETs) has gained an important part of the interest of researchers and become very popular in last few years. MANETs can operate without fixed infrastructure and can survive rapid changes in the network topology. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. This paper is subjected to the on-demand routing protocols with identical loads and environment conditions and evaluates their relative performance with respect to the two performance metrics: average End-to-End delay and packet delivery ratio. We investigated various simulation scenarios with varying pause times. From the detailed simulation results and analysis, a suitable routing protocol can be chosen for a specified network and goal.

Abstract

In this paper, a new type of MIN, New Irregular Augmented Shuffle Network (NIASN) is proposed. The proposed NIASN and existing IASN, QT, ASEN-2 MINs are analyzed and compared. The proposed network NIASN provides much better Bandwidth, Probability of acceptance, Throughput, Processor Utilization, and Processing Power than existing MINs.

Abstract

Abstract

Health care has become one of the most important services. Hospitals, physicians, insurers, and managed-care firms are networking, merging, and forming integrated organizations to finance and deliver health care. This paper mainly does some research on the problem of secure data flow. Data access is enabled via internet browser technology. Relevant patient and image acquisition information is extracted from the DICOM images and stored into a relational database. Patient information such as radiological findings are transferred from the Radiological Information System (RIS) into the database. Image data is accessed viewer. Since data security mechanisms either by a fast preview tool , a DICOM encryption of sensitive patient data is implemented. The method allows a dynamic selection of the data to be encrypted.

Abstract

Abstract

Abstract

In this paper we present a survey of various existing secure routing protocols for mobile ad hoc networks (MANETs). A mobile ad-hoc network is a self-configuring network of mobile nodes, connected by wireless links, which act as routers and are free to move randomly and organize themselves arbitrarily. These types of networks operate in the absence of any fixed infrastructure which makes them easy to deploy but it becomes difficult to make use of the existing routing techniques for network services. In order to facilitate communication within the network, a Routing Protocol (RP) is used to discover routes between nodes. The primary goal of such an ad-hoc network RP is correct and efficient route establishment between a pair of nodes so that messages may be delivered in time. The wireless and distributed nature of MANETs poses security a great challenge to system designers. This paper contains two main sections: first section presents a short literature study on various types of security attacks and routing security schemes that have been proposed to prevent and/or detect these attacks and second gives a state-or-the-art review of the existing secure Routing Protocols designed for MANETs with a comparison for typical representatives.

Abstract

The Performance of a system depends directly on the time required to perform an operation and number of these operations that can be performed concurrently. High performance computing systems can be designed using parallel processing. The effectiveness of these parallel systems rests primarily on the communication network linking processors and memory modules. Hence, an interconnection network that provides the desired connectivity and performance at minimum cost is required for communication in parallel processing systems. Multistage interconnection networks provide a compromise between shared bus and crossbar networks. In this paper, a new class of Irregular, Fault-tolerant multistage interconnection network named as Irregular Augmented Shuffle Network (IASN) has been proposed. The network has less number of stages as compared to existing irregular networks. Various performance parameters have been analyzed which shows better performance of the proposed network than the existing networks. The reliability of a network is evaluated in terms of MTTF. It has been the IASN has a higher MTTF for upper and lower bound in comparison bound in comparison to networks such as ASEN-2 and ABN and is comparable to FT network.

Abstract

Two pass routing scheme is described for communication in a multiprocessor system employing a unique-path multistage interconnection network in the presence of faults in the network. It is capable of tolerating all single faults and many multiple faults in all except the first and last stages of the network. The routing scheme is useful for tolerating both permanent as well as intermittent faults in the network. The hardware over head for implementing the scheme is very small and no time-penalty is paid in the fault-free case.