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Photovoltaic systems have become more popular as people become more interested in developing energy from renewable resources. Even after the installations, however, there is still a lack of understanding about the importance of inspecting the condition of the PV modules. To keep the PV running, early hot-spot detection is required. For detecting hot-spots, thermal imaging is still a popular technique. This research proposes to develop a method for detecting hot-spots in thermal images of photovoltaic modules using artificial intelligence techniques. Pre-processing, segmentation with an Artificial Neural Network and identification are the three main processes. The proposed method appears to be a good choice for improving hot-spot detection in PV monitoring systems, according to the results of the experiments.

In photovoltaic systems, maximum power point tracking (MPPT) methods are used to get the maximum power out of them. The presence of several peaks in a PV array’s power-voltage characteristics is due to partial shadowing conditions that increase the complexity of the tracking operation. In this paper, the Global Maximum Power Point (GMPP) is calculated using latest meta-heuristic optimization algorithm known as Jellyfish Search (JS). This unique MPPT approach is used to reduce PV module tracking time and improve the tracking efficiency. Using MATLAB/SIMULINK, the effectiveness of the suggested JS algorithm is assessed by contrasting it with the traditional P&O approach in terms of tracking speed and precision. The simulation findings indicate that the JS algorithm’s tracking ability is better than that of the conventional P&O method. In the experimental results, the JS algorithm reduces convergence time by 56.6% when compared to the PSO algorithm. Also, the proposed JS algorithm generates output power higher than the PSO MPPT algorithm using the duty cycle ratio value at the expected peaks.

Diabetic Macula Edema (DME) is brought about by collection of extracellular liquid from hyper penetrable vessels inside the macula. DME is one of the main sources of visual impairment among diabetic patients. The patients who are influenced by diabetes and Diabetic Retinopathy (DR) are well on the way to be influenced by DME. The presence of Exudates in macula area is the significant sign of essence of DME. The Optic Disc (OD) and Blood Vessels (BVs) are the retinal milestones divided to find macula and fovea locale. Area of macula focus is vital for portion the anomalies in the macula locale. The irregularity and its seriousness of the sickness in macula district can be distinguished solely after finding the macula place. Pictures from High Resolution Fundus (HRF) database are utilized for testing and examination. The Atanassov’s Intuitionistic fuzzy Sets (A-FIS) and morphological tasks are utilized to portion OD and BVs. With these retinal landmarks, macula is recognized by raphe assessment. The Exudates (EXs) present in macula district are portioned proficiently by inclination based versatile thresholding. Deep Convolution neural network used for classification of DME and non-DME affected images. The division of OD and EXs has accomplished SE of 95.21%, 98.2%, SPE of 82.36%, 97.4% and ACC of 99.21%, 99.23% separately.

Diabetic Retinopathy (DR) is an adverse change in retinal blood vessels leads to blindness for diabetic patients without any symptoms. Diabetes is characterized by imbalance level of glucose in the human body. The optic disc (OD) is the major retinal landmark. Localization of OD is an important step in fundus image analysis and to develop Computer Aided Diagnosis tool for DR. OD center detection is necessary to reduce false positive rate in the detection of exudates (EXs). EXs is the white lesion present in the retina which is the early symptom for the diagnosis of DR. OD is detected using intensity variation algorithm and EXs is segmented using inverse surface adaptive thresholding algorithm. This algorithm achieves better result in localizing OD and segmenting EXs when compared to literature-reviewed methods. The maximum intensity variance method is used to locate OD with average ACC of 96.54%, 98.65%, 98.12%, 99.23%, 99.81% and 98.47% in DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases with less computation time of 102 ms, 108 ms, 120 ms, 93 ms, 110 ms and 131 ms. The inverse surface adaptive thresholding method has achieved an SE of 97.43%, 98.87%, 99.12%, 97.21%, 98.72%, and 96.63%, a SPE of 91.56%, 92.31%, 90.21%, 90.14%, 89.58%, 92.56% and an ACC of 99.34%, 99.67%, 98.34%, 98.87%, 99.13%, 98.34% for DIARETDB0, DIARETDB1, MESSIDOR, DRIVE, STARE and Bejan Singh Eye Hospital databases respectively.

Cooperation among unlicensed secondary users was more significant in cognitive radio ad hoc networks (CRAHNs). The main challenges of CRAHNs now a day address the main issues of spectrum scarcity and underutilization. Due to the lack of channel sensing, cooperative spectrum sensing (CSS) is the main challenge in CRAHNs. Efficient CSS is more essential to enhance the detection of performance by utilizing the theoretical studies of spatially distributed secondary users (SUs). This paper uses the priority-based two-stage detection model (PBTSDM) to analyze the cooperation strategy among secondary users (SUs) and primary users (PUs). SUs in distributed CSS sense continuously amongst themselves and take unified decision on the presence or absence of PUs by using entropy-based energy detection mechanism. The simulation results reveal that the sensing accuracy in terms of sensing time and energy efficiency (in terms of minimum energy consumption) increased significantly by using the proposed scheme.

DAG scheduling is a process that plans and supervises the execution of interdependent tasks on heterogeneous computing resources. Efficient task scheduling is one of the important factors to improve the performance of heterogeneous computing systems. In this paper, an investigation on implementing Variable Neighborhood Search VNS algorithm for scheduling dependent jobs on heterogeneous computing and grid environments is carried out. Hybrid Two PHase VNS HTPHVNS DAG scheduling algorithm has been proposed. The performance of the VNS and HTPHVNS algorithm has been evaluated with Genetic Algorithm and Heterogeneous Earliest Finish Time algorithm. Simulation results show that VNS and HTPHVNS algorithm generally perform better than other meta-heuristics methods.

Cognitive Radio (CR) is a promising and potential technique to enable secondary users or unlicenced users to exploit the unused spectrum resources effectively possessed by primary users (PUs) or licenced users. The proven clustering approach is used to organize nodes in the network into logical groups to attain energy efficiency, network scalability, and stability for improving the sensing accuracy in CR through cooperative spectrum sensing. In this paper, a distributed dynamic load-balanced clustering algorithm is proposed. In this algorithm, each member in the cluster is to calculate the cooperative gain, residual energy, distance, and sensing cost from the neighboring clusters to perform the optimal decision. Each member in a cluster participates in selecting a cluster head through cooperative gain, and residual energy that minimise network energy consumption and enhances the channel sensing. First, we form the number of clusters using the Markov decision process model to reduce the energy consumption in a network. In this algorithm, CR users effectively utilize the PUs' reporting time slots when PUs are not available. The simulation results reveal that the cluster convergence, energy efficiency, and accuracy of channel sensing increased considerably by using the proposed algorithm.

Membrane proteins are vital type of proteins that serve as channels, receptors and energy transducers in a cell. They perform various important functions, which are mainly associated with their types. They are also attractive targets of drug discovery for various diseases. So predicting membrane protein types is a crucial and challenging research area in bioinformatics and proteomics. Because of vast investigation of uncharacterized protein sequences in databases, customary biophysical techniques are extremely tedious, costly and vulnerable to mistakes. Subsequently, it is very attractive to build a vigorous, solid, proficient technique to predict membrane protein types. In this work, a novel feature set Exchange Group Based Protein Sequence Representation (EGBPSR) is proposed for classification of membrane proteins with two new feature extraction strategies known as Exchange Group Local Pattern (EGLP) and Amino acid Interval Pattern (AIP). Imbalanced dataset and large dataset are often handled well by decision tree classifiers. Since imbalanced dataset are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification and Regression Tree (CART), ensemble methods such as Adaboost, Random Under Sampling (RUS) boost, Rotation forest and Random forest are analyzed. The overall accuracy achieved in predicting membrane protein types is 96.45%.

This paper presents a new approach to segment pre-retinal and sub-retinal haemorrhage from colour fundus retinal image based on improved toboggan segmentation. Effective segmentation of pre-retinal, sub-retinal, small/blot haemorrhage detection is important in the development of screening system of Diabetic Retinopathy (DR). The strategy involves the application of edge detection and super-pixel based toboggan segmentation. The main advantage of the proposed work is that it does not depend on the manually selected parameters. This haemorrhages detection technique leads to high accuracy of 93.21% in comparison with the ground truth images from ophthalmologist.

Human Eye is one of the most sophisticated organ, with cornea ,retinal pigment epithelium, bruch membrane, macula, vitreous body, retina and optic nerve. Retinal image analysis is broadly used for screening the patients affected from sight threatening eye diseases like Diabetic Retinopathy (DR) and glaucoma. Optic disc (OD) margin is the mandatory landmark in establishing a frame of reference of classifying retinal and optic nerve pathology. Trustworthy and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a fully automated method for OD segmentation algorithm developed for retinal disease screening. First, preprocessing step used to remove noise by illumination correction and contrast enhancement. Second, a thresholding technique using Atanassov’s intuitionistic fuzzy set (A-IFS) is employed. This approach uses A-IFS histon, an encrustation of the histogram consist of the pixels that belong to the set of similar pixels, in roughness index A-IFS histon & histogram correlated to upper and lower approximations and optimum threshold value is identified which pixel belong to background or to object(OD). This approach is evaluated by means of three publicly available databases DRIVE, MESSIDOR, DIARETDB0, the experimental outcome shows that the overall performance is with 99% correct optic disc localization.

Glaucoma is an eye disease that leads to vision loss without any major symptoms. Detecting the disease in time is important because it cannot be cured. Due to raised Intra-Ocular Pressure (IOP), the Optic Nerve Head (ONH) gets damaged and the assessment of ONH is more promising because it consists of Optic Disc (OD) and Optic Cup (OC) in retinal fundus image. OD and OC detection is important preprocessing step to diagnose glaucoma. In this paper, we proposed a segmentation of OD by FeatureMatch (FM) technique and OD is localized using medial axis detection. The correspondence between the OD reference image and any input image is found by FM. This correspondence provides a distribution of patches in the input image that are closest to patches in the reference image. The proposed work is evaluated using DRIONS-DB that consists of 110 images. The OD is correctly localized in more than 60 images. The performance measures such as Root Mean Square Error (RMSE) and Mean of Structural SIMilarity (MSSIM) are calculated. The average computational time for this proposed work is about 14 secs. The average RMSE of the proposed work is about 0.05389.

The comparative analysis of the renowned cryptographic algorithms AES, DES and RSA. The Rijndael algorithm was adapted as Advanced Encryption Standard (AES) algorithm, to Data Encryption algorithm (DES), which have been in the security standards since long time. The comparative analysis is implemented in IEEE 802.11i wireless platform. Compared to DES, AES contains CCMP which is a security standard that provides the highest level of security to encrypt and authenticate the data simultaneously. CCMP protocol is to provide robust security. The CCMP protocol is based on Advanced Encryption Standard (AES) encryption algorithm. It uses the Counter Mode with CBC-MAC (CCM) mode of operation. The CCM mode combines Counter (CTR) mode for privacy and Cipher Block Chaining Message Authentication Code (CBC-MAC) for authentication. The implementation is done on the NS-2 platform to compare and analyzes the performance of this with DES and RSA algorithms, based on the following three criteria: (a) Bit rate; (b) Packet delay; and (c) The number of packets. Thus the motivation is to provide a secure data transfer in the wireless medium in IEEE 802.11i.

Grid computing solves high-performance and high-throughput computing problems through sharing nodes ranging from personal computers to supercomputers distributed around the world. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. In this paper, an investigation on implementing Two-Phase Variable Neighborhood Search (TPVNS) algorithm for scheduling independent jobs on computational grid is carried out. The proposed algorithm consists of two modules with General Variable Neighborhood Search and Basic Variable Neighborhood Search algorithms in order to find a good mapping of grid jobs with grid nodes. The performance of the proposed algorithm has been evaluated with deterministic heuristic and evolutionary algorithms. Simulation results show that TPVNS algorithm generally performs better than the existing methods.

Predicting membrane protein types is an important and challenging research area in bioinformatics and proteomics. Traditional biophysical methods are used to classify membrane protein types. Due to large exploration of uncharacterized protein sequences in databases, traditional methods are very time consuming, expensive and susceptible to errors. Hence, it is highly desirable to develop a robust, reliable, and efficient method to predict membrane protein types. Imbalanced datasets and large datasets are often handled well by decision tree classifiers. Since imbalanced datasets are taken, the performance of various decision tree classifiers such as Decision Tree (DT), Classification And Regression Tree (CART), C4.5, Random tree, REP (Reduced Error Pruning) tree, ensemble methods such as Adaboost, RUS (Random Under Sampling) boost, Rotation forest and Random forest are analysed. Among the various decision tree classifiers Random forest performs well in less time with good accuracy of 96.35%. Another inference is RUS boost decision tree classifier is able to classify one or two samples in the class with very less samples while the other classifiers such as DT, Adaboost, Rotation forest and Random forest are not sensitive for the classes with fewer samples. Also the performance of decision tree classifiers is compared with SVM (Support Vector Machine) and Naive Bayes classifier.

Skin detection in images or videos plays a vital ro le in a wide range of image processing applications like face detection, face tracking, gesture analysis, hu man-computer interaction etc. Due to its high proce ssing speed and invariant against rotation, it has receiv ed significant interest in pattern recognition and computer vision. The robustness of skin detection using colo r information depends on real world conditions such as background, noise, change of intensity and lighteni ng effects. This situation can be improved by using texture as a descriptor to extract skin pixels in i mages. This study proposes color based skin detecti on algorithm (fusion of Cheddad’s approach with Cr of YC bCr color space) with a texture based skin location algorithm called Modified Weber’s Law Descriptions (MWLD) to evaluate region features. MWLD is based on the fact that the Human Visual System (HVS) is more sensitive in lumens contrast than absolut e luminance values. In MWLD, the differential excitat ion and gradient orientation of the current pixel a re considered to extract the texture features. For dif ferential excitation, Just Noticeable Distortion (J ND) is also used. Due to the absence of red component in C heddad’s new color space for skin detection, the detection rate is not good for real time applicatio ns. To improve this situation, the Cr component of YC bCr color space is included to obtain the color feature s. According to experimental results, the proposed method exhibits satisfactory performance in terms of True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy with wide variations in size, color and or ientation. Different window sizes for differential excitation such as 3◊3, 5◊5 and 7◊7 are also taken to check the performance.

Due to the development of information and network technologies, idle computers all over the world can be organized and utilized to enhance the overall computation performance. Grid computing refers to the combination of computer resources from multiple administrative domains used to reach a common goal. Grids offer a way of using the information technology resources optimally inside an organization. As the grid environments facilitate distributed computation, the scheduling of grid jobs has become an important issue. This study introduces a novel approach based on Biogeography Based Optimization algorithm (BBO) for scheduling jobs on computational grid. The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time. The performance of the proposed algorithm has been evaluated with Genetic Algorithm (GA), Differential Evolution algorithm (DE), Ant Colony Optimization algorithm (ACO) and Particle Swarm Optimization algorithm (PSO).

This paper presents a Filter Bank Multicarrier (FBMC), a viable waveform candidate for fifth generation (5G) communications using Staggered-Modulated Multitone (SMT). FBMC is preferred in optical communication because of its ability to work without Cyclic Prefix (CP). In any case, the operation of FBMC in optical access systems with Artificial Neural Networks (ANNs) has not been broadly explored either downstream or upstream. This work presents an advanced Nonlinear Feed-Forward Equalizer (NFFE) that makes use of multilayer ANN for dispersion compensation. ANN is trained to act as a filter with an extensive equalizer training which has the ability to mitigate dispersion and increase the performance of the system. The simulation work is used to study the performance of intensity modulated FBMC system with direct detection in Long Reach-Passive Optical Networks (LR-PONs).The transmission data rate is varied between 8 and 10[Formula: see text]Gbps with the optical fiber length from 30 to 90[Formula: see text]km of Standard Single Mode Fiber (SSMF). The obtained result suggests that FBMC system with ANN-NFFE equalizer fundamentally builds the resilience to the Chromatic Dispersion (CD) distortion, and a CP-less transmission is possible upto 90[Formula: see text]km.

The local or remote grid computing resources have been normally assigned the task of executing user jobs in a distributed environment. An important factor affecting grid processing is the network bandwidth that differs from network to network. In order to overcome this problem, a few typical algorithms are suggested keeping in view of computing grid resources based on load balancing across the grid system. These algorithms help in improving the performance to some extent, yet none of them has been found experimentally suitable to overcome the major short falls experienced in the grid computing system. A hierarchically distributed peer-to-peer (HDP2P) architecture is proposed to overcome all major problems encountered due to virtuality and heterogeneity. Results suggested that HDP2P architecture was of great use in balancing the load and allocating to the resources to the groups of jobs keeping MIPS accountability using grouping-based dynamic job scheduling algorithm (GBDJS). The proposed algorithm is compared with first come first served (FCFS) job scheduling algorithm and ant colony optimisation (ACO). There is a considerable improvement achieved in performance by using this novel architecture.

A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes that dynamically forms a temporary network without any fixed infrastructure. Each node participates in the ad hoc routing protocol that helps it to discover multi-hop paths through the network to any node. Security challenges have become a primary concern for these networks due to their characteristics such as open medium, dynamic topology, distributed collaboration and other various constraints like battery power and low bandwidth. An Authentication mechanism is certainly needed to prevent the various possible attacks by any malicious node. In this study, we propose an efficient authentication protocol, named CAODV for a MANET with the aid of cryptographic certificates. We implemented this CAODV protocol using the network simulator NS-2 and the simulation results reveal that this mechanism is highly effective even in the presence of large number of malicious nodes. Despite a considerable increase in the routing overhead, it is minimal and outweighed by the increased security services provided by our proposed CAODV protocol for MANET.

Locating the Optic Disc (OD) and Macula Center (MC) is the main step in the automatic extraction of retinal anatomical structures for Diabetic Retinopathy (DR). In this work, a variety of vessel segmentation methods have been adopted for the identification of OD in retinal images. In order to locate OD, different segmentation algorithms like matched filter, spatially weighed Fuzzy C-means and threshold are used to detect the blood vessels from the retinal image. Matching the expected directional pattern of the retinal blood vessels is proposed to locate the OD center. The proposed vessel direction filter is resized and OD center is measured. Macula center (fovea) is located at a constant distance from optic disc. A new method with geometric approach is proposed for macula detection. The proposed methods have been evaluated using a STARE and DRIVE datasets which contains both normal and diseased retina. The proposed methods are successfully adopted for segmentation of blood vessels and location of OD and MC in both normal and abnormal images.

The images usually bring different kinds of noise in the process of receiving, coding and transmission. In this paper the Curvelet transform is used for de-noising of image. Two digital implementations of the Curvelet transform (a multiscale transform) viz the Unequally Spaced Fast Fourier Transform (USFFT) and the Wrapping Algorithm are used to de-noise images degraded by different types of noises such as Random, Gaussian, Salt and Pepper, Speckle and Poisson noise. This paper aims at the effect the Curvelet transform has in Curvelet shrinkage assuming different types of noise models. A signal to noise ratio as a measure of the quality of de-noising was preferred. The experimental results show that the conventional Curvelet shrinkage approach fails to remove Poisson noise in medical images.

of text can be organized in an unsupervised manner. In this paper, Text document clustering is done based on query and its semantic relation. The method utilizes hypernymy to identify its relation. It was detected by using the Word Net. It act as background knowledge of the Query and provides its synonymic terms. This paper proposed the new term-document matrix called Query based document vector model, which is constructed using query with two terms and its hypernymy. The results show that our new measure Cluster Accuracy is significantly better to evaluate the quality of cluster and better results are obtained. General Terms Text mining and Information Retrieval, Partitioning M ethod KeywordsNoun, Word net, Query based document vector model, Hypernymy, Accuracy.

Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different texture images. Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. This paper, mainly focus on the unsupervised segmentation of color textured images as a prototypical application in computer vision. Here the objective is to group pixels or small image patches such that meaningful regions of identical/similar texture are obtained. This paper presents a new approach for color texture segmentation using Haralick features extracted from color co-occurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color co-occurrence matrices calculated in HSI color space. Fuzzified distance metric is used for achieving texture segmentation.

Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes that dynamically form a network temporarily without any support of central administration. Moreover, Every node in MANET moves arbitrarily making the multi-hop network topology to change randomly at unpredictable times. There are several familiar routing protocols like DSDV, AODV, DSR, etc...which have been proposed for providing communication among all the nodes in the network. This paper presents a performance comparison of proactive and reactive protocols DSDV, AODV and DSR based on metrics such as throughput, packet delivery ratio and average end-to-end delay by using the NS-2 simulator., Comment: 9 Pages,10 Figures, 3 Tables

Grid computing refers to the combination of computer resources from multiple administrative domains to reach common goal. Grids offer a way of using the information technology resources optimally inside an organization. Grid environments facilitate distributed computation. Hence the scheduling of grid jobs should be considered as an important issue. This paper introduces a novel approach based on Differential Evolution algorithm for scheduling jobs on computational grid. The proposed approach generates an optimal schedule so as to complete the jobs within a minimum period of time and utilizing the resources efficiently.

Noise removal is essential in medical imaging applications in order to enhance and recover anatomical details that may be hidden in the data. The issues of Poisson noise occurrence in medical imaging due to the arrival of photon nature of light produced at the time of capturing of image have always been a concern. This paper addresses a novel approach for the removal of Poisson noise embedded in the biomedical images based on multi-resolution transform-based denoising and fusion approach. In this technique, first, the images are denoised separately, by applying Discrete Wavelet Transform (DWT)/Stationary Wavelet Transform (SWT) and Fast Discrete Curvelet Transform (FDCT) integrated with Rudin-Osher-Fatemi (ROF) model. Next, the distinct features extracted from the denoised images are fused, resulting in an enhanced and denoised image. The proposed method was tested and compared with other denoising techniques on a set of biomedical images. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.