Design and analysis of text document clustering using salp swarm algorithm

The Journal of Supercomputing - In the technological era, exponential increase of unorganized text documents offers increased difficulties retrieving the most relevant data. The document clustering...     

Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder

The soft computing technique of fuzzy cognitive maps (FCM) for modeling and predicting autistic spectrum disorder has been proposed. The FCM models the behavior of a complex system and is used to develop new knowledge based system applications. FCM combines the robust properties of fuzzy logic and neural networks. To overwhelm the limitations and to improve the efficiency of FCM, a good learning method of unsupervised training could be applied. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is proposed here. Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem. The investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.     

A strategy to reduce the control packet load of MANETs with bidirectional links using DSR

Dynamic source routing (DSR) is a robust protocol commonly applied to multi‐hop wireless mobile ad hoc networks (MANETs). In this paper, an algorithm is proposed for modifying the basic DSR protocol to enhance its performance by reducing the number of redundant route reply packets (RREPs). In the modified DSR (MDSR), for a source destination pair, the destination responds to the first received route request packet (RREQ) with an RREP, and the subsequently received RREQs, bearing the same request ID, are responded to only if the hop count is less than that of all the previously received RREQs. The performance of MDSR has been compared with that of the basic DSR for different network densities and for different mobility of nodes. Simulation results show that MDSR gives fewer control packets, less latency and a higher packet delivery ratio than DSR. Copyright © 2007 John Wiley & Sons, Ltd.     

Synthesis,studies of 2-benzyl-amino-4-p-tolyl-6,7-di-hydro 5H-cyclo-penta–[b]pyridine-3 carbo-nitrile (BAPTDHCPCN) crystals for optical,photonic and mechano-electronic uses

2-benzyl-amino-4-P-tolyl-6,7-di-hydro 5H-cyclo-penta –[b]pyridine–3 carbo–nitrile—BAPTDHCPCAN crystal is grown by solution growth evaporation method and analysed for XRD, Unit Cell, Dielectric analysis, UV, Tauc’s plot and fluorescence and mechano studies. The single crystalline XRD data portrays the monoclinic nature of BAPTDHCPCAN with space group P2₁/c and a, b, c as 8.6828 Å, 17.7283 Å, 12.0403 Å with β as 94.254°. Chemical formula of crystal as C₂₃H₂₁N₃. The crystalline dimension is 14?×?12x7 mm³. The dielectric properties of the BAPTDHCPCAN have larger value at low range of frequencies. The phase matching effectiveness is analyzed and found to be 66 mV. The influx value of macro and thin film of BAPTDHCPCAN crystal is 4.2125 microns and 4.4336 microns as mid value before nano shaped influx. Absorption spectral two peaks centered at 248 nm at 325 nm, the band gap of crystal is calculated as 5.0 eV by photonic enhancement and the fluorescence represented emission for 399 nm. The BAPTDHCPCAN crystal is found to have represented –ve photoconductive nature. The BAPTDHCPCAN crystal specimen in of RISE [Reverse Indentation Size Effect] consequence with n greater than 2.

     

Hybridizing tabu search with ant colony optimization for solving job shop scheduling problems

The manufacturing industry continues to be a prime contributor and it requires an efficient schedule. Scheduling is the allocation of resources to activities over time and it is considered to be a major task done to improve shop-floor productivity. Job shop problem comes under this category and is combinatorial in nature. Research on optimization of the job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems. The neighborhoods are selected based on the strategies in the ant colony optimization with dynamic tabu length strategies in the tabu search. The inspiring source of ant colony optimization is pheromone trail that has more influence in selecting the appropriate neighbors to improve the solution. The performance of the algorithm is tested using well-known benchmark problems and is also compared with other algorithms in the literature.     

Community Detection Using Jaacard Similarity with SIM-Edge Detection Techniques

The structure and dynamic nature of real-world networks can be revealed by communities that help in promotion of recommendation systems. Social Media platforms were initially developed for effective communication, but now it is being used widely for extending and to obtain profit among business community. The numerous data generated through these platforms are utilized by many companies that make a huge profit out of it. A giant network of people in social media is grouped together based on their similar properties to form a community. Community detection is recent topic among the research community due to the increase usage of online social network. Community is one of a significant property of a network that may have many communities which have similarity among them. Community detection technique play a vital role to discover similarities among the nodes and keep them strongly connected. Similar nodes in a network are grouped together in a single community. Communities can be merged together to avoid lot of groups if there exist more edges between them. Machine Learning algorithms use community detection to identify groups with common properties and thus for recommendation systems, health care assistance systems and many more. Considering the above, this paper presents alternative method SimEdge-CD (Similarity and Edge between's based Community Detection) for community detection. The two stages of SimEdge-CD initially find the similarity among nodes and group them into one community. During the second stage, it identifies the exact affiliations of boundary nodes using edge betweenness to create well defined communities. Evaluation of proposed method on synthetic and real datasets proved to achieve a better accuracy-efficiency trade-of compared to other existing methods. Our proposed SimEdge-CD achieves ideal value of 1 which is higher than existing sim closure like LPA, Attractor, Leiden and walktrap techniques.     

An Enhanced Load Balancing Approach for Dynamic Resource Allocation in Cloud Environments

Cloud computing is one of the distributed resource-sharing technology that offers resources on a pay-as-you-use basis. Platform as a service, Infrastructure as a service, and Software as a Service are services provided by the Cloud. Each end user's Quality of service must be ensured by the cloud service provider. In recent days, cloud utilization is rapidly increasing. To avoid congestion and to preserve the Service Level Agreement, the large workload must be balanced across the network. In this research work, a new load balancing approach is proposed for the dynamic resource allocation process to improve stability and to increase profit. PBMM algorithm is devised for an effective load balancing process through which, resource scheduling is performed. Task size and the bidding value coded by each customer are taken into account. To optimize the waiting time, resource tables and task tables are employed. The average waiting time and response time of the special users are minimized. The simulation results show that the proposed load balancing technique ensures the maximum profit and it enhances load balancing stability by increasing the number of special users.

     

INVESTIGATION ON MAMMOGRAPHIC IMAGE COMPRESSION AND MICROCALCIFICATION ANALYSIS USING MULTIWAVELETS AND NEURAL NETWORKS

In digital mammography, the resulting electronic image is very large in size, which poses a significant challenge to the transmission, storage, and manipulation of images. Microcalcification is one of the earliest signs of breast cancer, and it tends to appear in small-sized, low-contrast radiopacities in the high-frequency spectrum of a mammographic image. Scalar wavelets excel multiwavelets in terms of peak-signal-to-noise ratio (PSNR), but fail to capture high-frequency information. This study proposes mammographic image compression and microcalcification detection in original and compressed reconstructed images using multiwavelets and neural networks. It also details the classification results obtained through the multiwavelet-based scheme in comparison with the existing scalar wavelet-based scheme. For a testing sample of 30 images, the proposed classification scheme outperforms the scalar wavelet-based classification by a sensitivity of 2.23% and specificity of 3.4% for original (uncompressed) images. Also, it increases the sensitivity by 2.72% and specificity by 8.4% for compressed reconstructed images. This increase in sensitivity and specificity demonstrates a better performance of the proposed detection scheme.     

Automated building and road classifications from hyperspectral imagery through a fully convolutional network and support vector machine

The Journal of Supercomputing - Hyperspectral imagery facilitates the determination of various urban-correlated characteristics, such as features on the Earth's surface, including roads, trees,...     

RETRACTED ARTICLE: Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

In medical field, securing every patient’s record is main concern, ascribed to many fraudulent cases occurring in the health sector. The data of every individual must be engraved and sent into end-user without any issues. Mainly in the healthcare industry, where thoughts are often focused on saving someone’s life and rightly so, but securing access to interfaces and computer systems that store private data like medical records is also an essential factor to consider. Data security is a corresponding action between controlling access to information while allowing free and easy access to those who need that information. Still few problems are focused by the physician in the health sector. Patient’s data should be kept securely in medical provider servers so that physicians can provide proper treatments. To ensure secure storage and access management, we propose a novel hybrid lightweight encryption using swarm optimization algorithm (HLE–SO).The proposed HLE–SO technique merge Paillier encryption and KATAN algorithm, which provides the lightweight features. Generally, the lightweight encryption algorithms are affected by the key space. We introduce the swarm optimization algorithm to optimize the key space by changing the number of iteration round. Our main goal is to encrypt the medical data (EEG signal) and send to end user by utilizing proposed HLE–SO method. Finally, the implementation is done with MATLAB tool with different EEG signal data set. The simulation results of proposed HLE–SO technique is compared with the existing state-of-art techniques in terms of different performance metrics are MSE, PSNR, SSIM, PRD, encryption time and decryption time.