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This paper uses radio frequency signals for non-contact tracking and localization utilising Radio Frequency Identification (RFID) technology. The clustering approach is quite useful when dealing with large RFID readers. This method stimulates the expansion of RFID nodes without compromising the overall network performance. The Cluster Head (CH) is the most critical node in a clustered RFID system. This research presents a method for dynamically selecting the cluster head that takes the connectivity and power of each RFID reader into account. In dynamic mode, selecting a new cluster head is based on Fuzzy Logic. Based on these data, the energy level of 0.443 and centrality of 0.809 are the thresholds at which a node has a 91.8 % chance of becoming a CH. Managing massive RFID networks is also handled, with cluster numbers increasing at different intervals. The RFID network’s effectiveness is determined by measuring throughput, accuracy, delay, success, and error rates. The network may accommodate up to a thousand nodes with 13 node leaders for improved capacity. The results show a 97.8 % success rate, 0.22 % accuracy, a 2.64 % error rate, and a 36.91 second latency.

Karyotyping is a procedure to diagnose birth defects using chromosome pair. During the Karyotyping chromosomes arranged based on the length and each chromosome will be paired based on various parameters such as, chromosome length, banding pattern and Centromere position. Many methods are proposed to identify the above parameters to improve the Karyotyping accuracy. Since, it’s a challenging task for researchers to improve the accuracy of Karyotyping compared with clinical assays. In this paper, a novel computer geometry method is proposed for chromosome Karyotyping using inbuilt deep learning models with algorithms for chromosome segmentation, overlapped separation, banding pattern analysis and classification of chromosomes. Chromosome classification is carried out using various deep learning models and automated Karyotyping is carried out without manual intervention and model improved the Karyotyping accuracy over the existing methods. In this paper novel computer geometry method is proposed to automate the chromosome analysis in Matlab environment. The developed software provides the accuracy of 99.68% in classification and karyotyping.

Glaucoma and Stargardt's, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt's disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona--Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt's disease with retinal fundus images. The DNP-MDMSMSIC method comprises diverse types of layers that support to identify early detection of disease with improved accuracy and less time. Process as explained; initially, numerous qualified retinal images are given as input to the input layer. These input images are transmitted further to the hidden layer 1 to perform image pre-processing. In DNP-MDMSMSIC, Space-Variant Perona--Malik Diffusive Image Preprocessing is carried out to decrease the noise from input image without removing contents like edges, lines, etc., for image interpretation with a higher peak signal-to-noise ratio. This preprocessed image is further processed in the hidden layer 2 where the feature extraction process is performed to extract features like color, texture, and intensity with a higher degree of accuracy. Based on the extracted features, an input feature image gets segmented in hidden layer 3. Mean Shift Mode Seeking Segmentation algorithm is employed to segment the pixels in image space with corresponding feature space points. Then the segmented images are given to the output layer to perform retinal fundus image classification using Bregman Divergence Function. During the image classification, the distance between two segmented regions (i.e., testing image region of particular class and training image region) with convex is measured. In this way, the retinal fundus images get classified with higher accuracy. Experimental evaluation is performed by considering the metrics such as peak signal-to-noise, disease detection accuracy, disease detection time, and error rate corresponding to the number of retina fundus images and image size. DNP-MDMSMSIC method is designed to detect Glaucoma and Stargardt's disease at an earlier stage with higher accuracy by 8% and less time by 20% with aid of ACRIMA database. [ABSTRACT FROM AUTHOR]

Chromone-3-ylmethyl aryl ethers with unsubstituted orthopositions have been found to undergo a novel domino [1,3]-[1,3]-rearrangement to give 4'-hydroxyhomoisoflavones under thermal conditions while the para-substituted ethers lead to 2'-hydroxyhomoisoflavones involving an O- to C-[1,3]-migration, instead of the expected Claisen rearrangement. A few cishomopterocarpans have been synthesized using the 2'-hydroxyhomoisoflavones obtained from the thermal [1,3]-rearrangement of chromene-3-ylmethyl aryl ethers. [ABSTRACT FROM AUTHOR]