Soumen Banerjee

Cuckoo search coupled artificial neural network in detection of chronic kidney disease

In the present work a Cuckoo Search (CS) trained Neural Network (NN) or NN-CS based model has been proposed to detect Chronic Kidney Disease (CKD) which has become one of the newest threats to the developing and undeveloped countries. Studies and surveys in different parts of India have suggested that CKD is becoming a major concern day by day. The financial burden of the treatment and future consequences of CKD could be unaffordable to many if not detected at an earlier stage. Motivated by this, the NN-CS model has been proposed which significantly overcomes the problem of using local search based learning algorithms to train NNs. The input weight vector of the NN is gradually optimized by using CS to train the NN. The model has been compared with well-known classifiers like Multilayer Perceptron Feedforward Network (MLP-FFN) (trained with scaled conjugate gradient descent) and also with NN supported by Genetic Algorithm (NN-GA). The performance of the classifiers has been measured in terms of accuracy, precision, recall and F-Measure. The experimental results suggest that NN-CS based model is capable of detecting CKD more efficiently than any other existing model.

A Compact Half-Mode SIW Based Semi-circular Antenna with X-Shaped Slot

A half mode substrate integrated waveguide (HMSIW) based semi-circular antenna is chosen which operates at 5.22 GHz within the IEEE 802.11a WLAN UNII-1 band. The same antenna is miniaturized by the inclusion of x-shaped slot. The compact antenna is found to operate at the center frequency of 4.35 GHz thereby witnessing a shift of 870 MHz from 5.22 GHz.

Biomedical image enhancement based on modified Cuckoo Search and morphology

This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCullochs method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work.

Non-dominated sorting genetic algorithm — II supported neural network in classifying forest types

Pixel classification in land scape images has been found to be challenging. The problem becomes more challenging in forest images due to the similar spectral features of pixels situated close to each other. Geographically weighted variables have been employed to classify the two different species namely Cryptomeria japonica (Japanese Cedar or Sugi) and Chamaecyparisobtusa (Japanese Cypress or Hinoki) and one mixed forest class. Previous attempts have shown reasonable improvement in this task using Genetic Algorithm supported Neural Network over other traditional approaches. Motivated by this, a NSGA — II supported Neural Network (NN-NSGA — II) classifier is proposed. The proposed model has been compared with GA-NN (ANN trained with Genetic Algorithm with a single objective function) classifiers in terms of confusion matrix based performance metrics such as accuracy, precision, recall and F-Measure. Experimental results have indicated that the proposed NN-NSGA-II model is superior to the GA-NN model to a greater extent.

HMSIW based equilateral triangular and semi-circular antennas for WiMAX applications

In this paper, half-mode substrate integrated waveguide (HMSIW) based equilateral triangular and semicircular antennas have been proposed and designed to operate at 3.5 GHz WiMAX frequency. Two sides of the triangular antenna and the circumference of the semi-circular antenna are made perfect electrical conductor (PEC) wall by placing arrays of metallic vias. The side of both the antennas devoid of vias acts like a perfect magnetic conductor (PMC) wall. The triangular HMSIW antenna excites TE101 mode while thesemi-circular HMSIW antenna excites TM010 efficiently. Both the antenna prototypes are designed using Arlon AD270 substrate.

Detection of skin disease using metaheuristic supported artificial neural networks

Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

Multi-slot Loaded Dual Band Compact Half-Mode SIW Triangular Antenna

An equilateral triangular antenna resembling half-mode SIW structure and operating at 5.28 GHz WLAN UNII-2 band resonating frequency is considered. The same antenna resonates at the first harmonic frequency of 9.57 GHz. The antenna is made compact and designed to support dual-band operation with insertion of inverted V-shaped slot and a horizontal rectangular slot. The new antenna exhibits dual-band operation at the frequencies of 4.3 and 9.45 GHz. A shift of 980 MHz for the fundamental frequency and 120 MHz for the first harmonic is witnessed.

Dehazing technique for natural scene image based on color analysis and restoration with road edge detection

Outdoor scene visibility deteriorates due to presence of haze or fog. Several dehazing techniques have found its application in fields of surveillance, detection, restoration and tracking. The techniques proposed till date are computationally complex and time consuming and thus not suited for real-time applications. The degraded images mostly suffer from reduced contrast. The technique proposed in this paper concentrates on fast restoration of scene color and contrast based on color analysis of the scene objects. As the image detail suffers due to haze, the road edge of an outdoor scene degrades significantly. Thus the proposed model has been extended to road edge detection and tracing along with on road obstacle detection.

Quarter-mode SIW based compact triangular antenna for 2.5 GHz cellular spectrum

In this paper, a quarter mode SIW based co-axial fed triangular antenna has been designed to operate at 2.57 GHz spectrum. Metallic vias are placed at the bottom edge of the triangular antenna to make it PEC wall while the other sides devoid of metallic vias resembles PMC walls. The antenna is miniaturized by the insertion of a narrow rectangular slit placed horizontally near the centre of the triangle. The compact antenna exhibits a shift in the resonating frequency from 2.57 GHz to 2.51 GHz i.e., a shift of 60 MHz. Both the antennas are designed using Arlon AD270 substrate and the antenna parameters are studied.

A compact dual-band half-mode SIW based semi-circular antenna

In this paper, a half mode SIW based semi-circular antenna with co-axial feed has been designed for dual band operation. The fundamental resonating frequency is found to operate at 4.66 GHz while the next higher mode is at 8.3 GHz. The antenna performance is optimized through parametric studies and the dual band antenna is designed using Arlon AD270 substrate. Simulation studies have been carried out to study the various antenna parameters.