Sri Ramakrishna Engineering College

About: Sri Ramakrishna Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Control theory. The organization has 1030 authors who have published 843 publications receiving 3822 citations.

Effect of Organic Additives on the Characteristics of Al/Organic Additive:ZrO 2 /p-Si Metal–Insulator-Semiconductor (MIS) Type Schottky Barrier Diodes

Abstract: In this work, zirconium dioxide (ZrO2) thin films were prepared on glass substrates with organic additives such as polyethylene glycol-6000 (PEG), polysorbate-80 (P80), triton X-100 (T100), citric acid (CA), tartaric acid (TA), and oxalic acid (OA) by sol–gel spin coating technique. The prepared thin films were annealed at 600 °C and their structural, morphological, optical and dc electrical properties were studied. From the XRD pattern, the crystal structure of the ZrO2 films was found to be monoclinic. The SEM micrographs of PEG:ZrO2 and CA:ZrO2 thin films exhibited rod-shaped and square-shaped grains, respectively. The EDX analysis confirmed the presence of Zr and O elements in the ZrO2 films. The UV–Vis analysis showed a higher transmittance for the PEG:ZrO2 and T100:ZrO2 films. The optical band gap (Eg) varied in the range of 5.66–5.83 eV. DC electrical conductivity (σdc) increased with a decrease of activation energy (Ea) for organic additive:ZrO2 thin films compared to pure ZrO2. A maximum value of σdc was noticed for the PEG:ZrO2 thin film. Metal–insulator-semiconductor type Schottky barrier diodes (Al/organic additive:ZrO2/p-Si) were fabricated and their electrical characteristics were studied. Results showed that the barrier height (ΦB), series resistance (Rs) and ideality factor (n) decreases for Al/organic additive:ZrO2/p-Si SBDs from that of the Al/Pure ZrO2/p-Si.

Optimized radial basis neural network for classification of breast cancer images

Abstract: Breast cancer is a curable disease if the diagnosed at an early stage. The chances of having breast cancer are the lowest in the married woman after the breast-feeding phase because the cancer is formed from the blocked milk ducts. Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence-based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by image analysing, detection, screening, diagnosing, and other performance measures. The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose. Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature. Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter and the classifier extracts the feature based on the breast image.

Particle Swarm Optimization (PSO) with fuzzy c means (PSO‐FCM)–based segmentation and machine learning classifier for leaf diseases prediction

Abstract: This paper proposes an automatic classification technique that uses leaf images some medicinal plants. It is primarily the core reason that drives the research presented here, including the introduction of new innovative segmentation and classification techniques that are deployed to facilitate automatic detection. The major aim of the work is to introduce a new leaf disease prediction technique. The study conducted here a unique but effective image segmentation, feature extraction, as well as plant leaf disease classification. The proposed approach initially preprocesses leaf images of plants thereafter which the diseased sections of the plant are segmented by deploying Particle Swarm Optimization (PSO)–based fuzzy c means segmentation (PSO‐FCM), Gaussian Mixture Model (GMM)–based background subtraction. Vein and shape features, edge‐based feature extraction, and texture characteristics or texture features (TF) are computed. This methodology classifies the leaves of medicinal plants by deploying the Multiple Kernel Parallel Support Vector Machine (MK‐PSVM) classifier. The classifier is implemented via the use of MATLAB classifier. The results are measured using the accuracy, sensitivity, specificity, precision, and F‐measure metrics. Experimental results depict that the classifiers that have been proposed here achieve a higher classification accuracy enabling leaf detection.

Delay Fault Testing of VLSI Circuits

Abstract: The advances in VLSI technology have resulted in devices with millions of transistors thus creating new test challenges Moore’s law states that the scale of ICs has doubled every 18 months Reduction in feature size increases the speed of integrated circuits, thus increasing the probability that a manufacturing defect in the IC will result in a faulty chip