Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer-deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.     

Reply to comment on “Dependence on charge transfer band and emission properties by the crystal chemistry of A- and B-site cations in Eu3+-doped quaternary pyrochlore-type red phosphors,Ca(RE)1−x(M)NbO7 (RE=Y,Gd; M=Ti,Sn)”

Journal of Materials Science: Materials in Electronics -     

Multimedia utilization of non-computerized disguised voice and acoustic similarity measurement

Multimedia Tools and Applications - In this paper, an identification of a speaker for multimedia application under non-electronically disguised voice is performed. In non-electronically disguised...     

Performance Analysis of MSFRLS-VFF Based Real-Time Adaptive Noise Canceller with RLS and APA Algorithms Using TMS320C6713 Processor

Wireless Personal Communications - In this paper, the real-time adaptive noise canceller (ANC) system is implemented using modified sigmoid function variation based recursive least square with...     

ZLBM: zero level binary mapping technique for video security

In recent days, providing security to data is a crucial and critical task in many image processing applications. Specifically, video security is an important and demanding concept. For this purpose, some of the embedding, encoding and decoding techniques are mentioned in existing works, but it has some drawbacks such as increased time complexity, computational complexity and memory consumption. Moreover, it does not provide high security during video transmission. To overcome all these issues, a new technique, namely, Zero Level Binary Mapping (ZLBM) is proposed in this paper for video embedding scheme. The motivation of this paper is to provide high security during video transformation by using the video steganography technique. At first, the cover and stego videos are given as the inputs and it will be converted into the video frames for further processing. Here, the Fuzzy Adaptive Median Filtering (FAMF) technique is employed to remove the impulse noise in the video frames. Then, the pixels in the filtered frames are grouped by using the block wise pixel grouping technique. After that, the frames are embedded with the help of ZLBM technique and encoded based on the patch wise code formation technique. On the receiver side, the inverse ZLBM and block wise pixel regrouping techniques are applied to get the original cover and stego videos. The novel concept of this paper is the use of ZLBM and patch wise code formation techniques for video embedding and compression. The main advantages of the proposed system are high security, good quality and reduced complexity. The experimental results evaluate the performance of the proposed video embedding technique in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Compression Ratio (CR), Bits Per Pixel (BPP) and Signal-to-Noise Ratio (SNR).     

Bilingual text detection from natural scene images using faster R-CNN and extended histogram of oriented gradients

Pattern Analysis and Applications - In today's world, scene text detection is important for a wide range of scientific and industrial processes. Compared with text detection in documents, text...     

Design and Analysis of a Fractal Antenna Using Jeans Material for WiMax/WSN Applications

This paper presents a novel approach in designing fractal antennas operating at 2.45 GHz. Mainly focuses on design and simulation of a combination of Koch and Sierpinski fractal antenna with jeans as substrate material with dielectric constant 1.6 up to 2nd iteration. The design of antenna presented here can radiate at 2.45 GHZ, 3.64 GHZ, and 4.06 GHz frequencies with a reflection coefficient less than  ? 10 dB, VSWR between 1 and 2 and enhancement in bandwidth up to 200 MHz. This antenna has been simulated using CADFEKO software, fabricated and tested on VNA and has been observed that there is good conformity between simulation and experimental results. Besides that, Fractal antenna using jeans material as substrate for WiMAX/WSN Application at S and C frequency bands is small in size, flexible, less costly and low profile multiband antenna.

     

Biaxially oriented films prepared from poly(vinyl chloride)/poly(methyl methacrylate co imide) blends

A study was conducted of blends of poly(vinyl chloride) (PVC) with a poly(methyl methacrylate co imide). The latter polymer was found to be miscible in PVC and to raise the glass transition temperature of the blend. Blends of all compositions could be oriented, but the processing temperature increased in proportion with T_g. For a given blend composition, orientation increased with increasing stretch ratio and strain rate and with decreasing stretch temperature. Increasing copolyimide content and increasing orientation generally lead to improved mechanical properties, though the blends containing high levels of copolyimide exhibited low ductilities.     

Immune response to chemically modified proteome

Both enzymatic and nonenzymatic PTMs of proteins involve chemical modifications. Some of these modifications are prerequisite for the normal functioning of cell, while other chemical modifications render the proteins as “neo-self” antigens, which are recognized as “non-self” leading to aberrant cellular and humoral immune responses. However, these modifications could be a secondary effect of autoimmune diseases, as in the case of type I diabetes, hyperglycemia leads to protein glycation. The enigma of chemical modifications and immune response is akin to the “chick-and-egg” paradox. Nevertheless, chemical modifications regulate immune response. In some of the well-known autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, and multiple sclerosis, chemically modified proteins act as autoantigens forming immune complexes. In some instances, chemical modifications are also involved in regulating immune response during pathogen infection. Further, the usefulness of proteomic analysis of immune complexes is briefly discussed.     

Alkaline earth metal-based catalyst and process for selective hydrocarbon conversion to acetylene and carbon monoxide

A multi-step process that employs a Ni-modified mixed alkaline earth metal oxides (AEMO) has been developed for the selective conversion of hydrocarbons to C₂H₂ and CO. The initial process step is the catalytic decomposition of a gaseous hydrocarbon mixture at an elevated temperature (ca. 800 °C) over Ni/AEMO, generating H₂, trace CO, carbon (C), and trace alkaline earth metal carbide (MC₂). The Ni/AEMO/C/MC₂ material is then heated to consume the remaining carbon, generate more MC₂, and evolve CO. Then, Ni/AEMO/MC₂ is cooled and reacted with excess H₂O at a low temperature (20 < T (°C) < 80) to selectively generate C₂H₂ and Ni/AEM(OH)₂·zH₂O. In the final process step, Ni/AEM(OH)₂·zH₂O is decomposed to yield H₂O and Ni/AEMO, which is recycled within the process. The most advanced Ni/AEMO materials developed thus far exhibit intrinsic production capacities exceeding 2000 μmoles of C₂H₂ per gram of Ni/AEMO per process cycle.