Sentiment Analysis (SA) is a technique for categorizing texts based on the sentimental polarity of people's opinions. This paper introduces a sentiment analysis (SA) model with text and emojis. The ...two preprocessed data's are data with text and emojis and text without emojis. Feature extraction consists text features and text with emojis features. The text features are features like N-grams, modified Term Frequency-Inverse Document Frequency (TF-IDF), and Bag-of-Words (BoW) features extracted from the text. In classification, CNN (Conventional Neural Network) and MLP (Multi-Layer Perception) use emojis and text-based SA. The CNN weight is optimized by a new Electric fish Customized Shark Smell Optimization (ECSSO) Algorithm. Similarly, the text-based SA is carried out by hybrid Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) classifiers. The bagged data are given as input to the classification process via RNN and LSTM. Here, the weight of LSTM is optimized by the suggested ECSSO algorithm. Then, the mean of LSTM and RNN determines the final output. The specificity of the developed scheme is 29.01%, 42.75%, 23.88%,22.07%, 25.31%, 18.42%, 5.68%, 10.34%, 6.20%, 6.64%, and 6.84% better for 70% than other models. The efficiency of the proposed scheme is computed and evaluated.
Denoising is an indispensable task to restore the image features from the corrupted low-quality images and improve the perceptual quality of images. Besides significant advantages in the field of the ...denoising images, several kinds of literature face difficulties in reducing the impulse noise in the image. This work designs a novel filter through the hybridization of the fuzzy filter and the Non-Local Means (NLM) filter. The proposed scheme removes the impulse noise in the images in two stages 1) noise identification, and 2) denoising stage. Noise identification is made by constructing the binary map based on the Support Vector Neural Network (SVNN) classifier. The SVNN classifier is trained based on the Genetic Algorithm (GA) for identifying the optimal weights and the bias. The features for the training are extracted from the images, and thus, the training procedure differentiates the noisy pixel from the good pixel. At the denoising stage, the hybrid filter is enabled to remove the impulse noise in the image. The proposed model uses the five standard images, such as Baboon, cameraman, Lena, peppers, and Pemaquid Point Lighthouse image for the experimentation purpose. From the simulation results, it is evident that the proposed hybrid filter along with the SVNN classifier achieved improved results with the values of 47.278 dB, 0.978 and 61.637 dB for the PSNR, SSIM, and the SDME, respectively.
Image denoising gains more attention in the field of image processing, which is essential to sustain the originality of the digital images in order to preserve all the essential information buried in ...the image. Even though lots of denoising techniques are available, the existing methods failed to denoise the image efficiently, and they are applicable only with lower noise probability. Thus, this paper proposes a Fuzzy Firefly Bayes Filter (FFBF) to perform the noise identification and removal. FFBF employs the Ck-based firefly Bayes algorithm and probabilistic clustering for identifying the presence of noisy pixel in the input image. The Ck-based Firefly Bayes algorithm is newly proposed by integrating the cuckoo search optimization, firefly optimization, and Bayes Classifier and it is based on the maximum posterior probability objective function. The proposed algorithm provides the best solution for the formulation of the binary matrix using the Bayes Classifier, which is subjected to fuzzy-based image denoising. The paper uses two standard images for experimentation, and the comparative analysis is performed in order to determine the superiority of the proposed method. The PSNR, SSIM, and SDME obtained for the proposed method are greater when compared with the existing methods, and the proposed method attained a maximum PSNR, SSIM, and SDME of 45.1696 dB, 0.8260, and 59.9684 dB.
Cancer is a common disease with an increasing mortality rate in recent years. Lung cancer is the most common cancer in men and women alike. It is caused by uncontrolled cell development in the lungs. ...These cells are divided into two types: benign and malignant. Benign tumours are usually harmless, do not spread to other cells, and have a smooth and regular shape, whereas malignant tumours can be dangerous and spread to other body cells to form a new cancerous nodule with an uneven shape. If lung cancer is detected early, it can be treated. Lung cancer symptoms typically appear in the human body when it is in its final stage, but advanced technology and computer-aided systems can detect it at an early stage. Currently, numerous conventional and machine learning techniques are used for such automated detection systems to detect lung cancer in its early stages, but such automated detection systems do not provide accurate detection and the processing of lung cancer detection takes a long time. As a result, a novel method for detecting lung cancer that employs deep learning techniques for accurate detection while requiring less computation time is proposed. CT images are used in this study because they have less noise disturbance than MRI and X-ray images. Median filtering and patch processing are used to improve image quality on such CT scans. These pre-processed images are then subjected to a clustering segmentation process, which segments the image and feeds it to a CNN classifier. For feature extraction and classification, CNN architecture is used. In the future extraction section, various low-level and high-level features are extracted. The classification layer is in charge of determining whether the provided image contains a malignant, benign, or normal tumour. Finally, statistical parameters like MSE, PSNR, Accuracy, Sensitivity, Specificity, and others were computed and combined with the existing system in this work.
A macrocyclic hydrazone Schiff base was synthesized by reacting 1,4-dicarbonyl phenyl dihydrazide with 2,6-diformyl-4-methyl phenol and a series of metal complexes with this new Schiff base were ...synthesized by reaction with Co(II), Ni(II) and Cu(II) metal salts. The Schiff base and its complexes have been characterized by elemental analyses, IR,
1H NMR, UV–vis, FAB mass, ESR spectra, fluorescence, thermal, magnetic and molar conductance data. The analytical data reveal that the Co(II), Ni(II) and Cu(II) complexes possess 2:1 metal–ligand ratios. All the complexes are non-electrolytes in DMF and DMSO due to their low molar conductance values. Infrared spectral data suggest that the hydrazone Schiff base behaves as a hexadentate ligand with NON NON donor sequence towards the metal ions. The ESR spectral data shows that the metal–ligand bond has considerable covalent character. The electrochemical behavior of the copper(II) complex was investigated by cyclic voltammetry. The Schiff base and its complexes have also been screened for their antibacterial (
Escherichia coli,
Staphylococcus aureus,
Shigella dysentery,
Micrococcus,
Bacillus subtilis,
Bacillus cereus and
Pseudomonas aeruginosa) and antifungal activities (
Aspergillus niger,
Penicillium and
Candida albicans) by MIC method. The brine shrimp bioassay was also carried out to study their
in-vitro cytotoxic properties.
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In this study, we investigate a long-term modulation in the relationship between Indian summer monsoon rainfall with the subsequent Australian summer monsoon rainfall. The two monsoon rainfall time ...series are significantly correlated at 0.3 at the 99 % confidence level. However, the relationship weakens during the 1932–1966 period, with the inter-monsoon correlation for the period falling below statistical significance. We find that this modulation is consistent with a breakdown of the typical El Niño-Southern Oscillation (ENSO) influence on sea surface temperature in the northern region of Australia, during this period. In addition, a change in the relative influences of ENSO and Indian Ocean Basin-wide Warming sea surface temperature anomalies on the Australian summer monsoon rainfall is also apparent across different time periods.
Regular exercise can counteract the adverse effects of aging on the musculoskeletal and cardiovascular systems. In males, the normal aging process is associated with reductions in testosterone ...production and impaired spermatogenesis, but the underlying mechanisms and their potential modification by exercise are unknown. Here, we report that lifelong regular exercise (running) protects the testes against the adverse effects of advancing age, and that this effect of running is associated with decreased amounts of oxidative damage to proteins, lipids, and DNA in spermatogenic and Leydig cells. Six-month-old male mice were divided into a sedentary group and a group that ran an average of 1.75 km/day, until the mice reached the age of 20 months. Seminiferous tubules of runners exhibited a full complement of cells at different stages of the spermatogenic process and a clear central lumen with large numbers of spermatozoa, in contrast to sedentary mice that exhibited disorganized spermatogenic cells and lacked spermatocytes in a central lumen. Levels of protein carbonyls, nitrotyrosine, lipid peroxidation products, and oxidatively modified DNA were significantly greater in spermatogenic and Leydig cells of sedentary mice compared with runners. These findings suggest that lifelong regular exercise suppresses aging of testes by a mechanism that involves reduced oxidative damage to spermatogenic and Leydig cells.
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