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Data stored on physical storage devices and transmitted over communication channels often have a lot of redundant information, which can be reduced through compression techniques to conserve space and reduce the time it takes to transmit the data. The need for adequate security measures, such as secret key control in specific techniques, raises concerns about data exposure to potential attacks. Encryption plays a vital role in safeguarding information and maintaining its confidentiality by utilizing a secret key to make the data unreadable and unalterable. The focus of this paper is to tackle the challenge of simultaneously compressing and encrypting data without affecting the efficacy of either process. The authors propose an efficient and secure compression method incorporating a secret key to accomplish this goal. Encoding input data involves scrambling it with a generated key and then transforming it through the Burrows-Wheeler Transform (BWT). Subsequently, the output from the BWT is compressed through both Move-To-Front Transform and Run-Length Encoding. This method blends the cryptographic principles of confusion and diffusion into the compression process, enhancing its performance. The proposed technique is geared towards providing robust encryption and sufficient compression. Experimentation results show that it outperforms other techniques in terms of compression ratio. A security analysis of the technique has determined that it is susceptible to the secret key and plaintext, as measured by the unicity distance. Additionally, the results of the proposed technique showed a significant improvement with a compression ratio close to 90% after passing all the test text files.

Animal and poultry feeds majorly constitutes of cereal grains and their by-products representing an excellent substrate for fungal colonization and mycotoxin contamination. Additionally, ecological factors play a crucial role in the process of fungal colonization in stored food/feed material. Here, we propose the physiological behavior of Fusarium proliferatum MYS9 in response to the combinational effects of different abiotic factors such as light, temperature, solutes and relative humidity. Red and blue spectral lights had stimulating effect on the growth and biomass and also Fumonisin B1 production recording 141.60 µg and 69.77 µg/g feed, respectively. The radial growth and biomass formation of F. proliferatum declined with the increasing ionic solute potential while the non-ionic solute potential showed an irregular pattern of growth and biomass. The room temperature (28 °C) showed maximum colony diameter (7.78 cm), biomass (1.32 g per 50 mL medium) and FB1 biosynthesis (913.25 µg/g feed). A significant radial growth was observed at 96% relative humidity and a high biomass of 0.82 g per 50 mL medium was observed at 98% humidity. A maximum growth of the fungus was observed at 28 °C in combination of -51.02 bars of osmotic pressure while the lower and higher temperature ranges with high osmotic pressures restricted the growth. The present study provides useful data facilitating a better understanding of the influence of ecological factors on fungal growth and toxin production in feeds and help to optimize storage measures and prevention strategies.

Probiotics are vital and beneficial organisms which offers the health benefits to the host organisms. The fungal probiotic field is one of the developing fields nowadays. Yeast has an enormous and diverse group of microorganisms that is attracting and expanding the attention from researchers and industries. Saccharomyces boulardii, the only patented strain belonging to yeast genera for the human use, has been broadly evaluated for its probiotic effect. Yeasts belonging to the genera Debaryomyces, Pichia, Yarrowia, Meyerozyma, Kluyveromyces etc.., have attained more interest because of their beneficial and probable probiotic features. These yeast probiotics produce VOCs (Volatile organic compounds), mycocins and antimicrobials which shows the antagonistic effect against pathogenic fungi and bacteria. Additionally, those yeasts have been recorded as good plant growth promoting microorganisms. Yeast has an important role in environmental applications such as bioremediation and removal of metals like chromium, mercury, lead etc., from waste water. Probiotic yeasts with their promising antimicrobial, antioxidant, anticancer properties, cholesterol assimilation and immunomodulatory effects can also be utilized as biotherapeutics. In this review article we have made an attempt to address important yeast probiotic attributes.

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

The present investigation was conducted to magnitude the genetic variability, heritability and genetic advance in snake gourd for identifying desirable parents. Fifty genotypes were evaluated for eleven characters through RBD during the year 2011–12. The phenotypic coefficient of variance (PCV) was slightly higher than their corresponding genotypic coefficient of variance (GCV) for all characters. Highest GCV and PCV values were observed for characters like fruit length, fruit weight and number of fruits per plant. Moderate phenotypic coefficient of variation and genotypic coefficient of variation for yield, fruit girth and number of seeds per fruit indicated the presence of high genetic variability for these traits in the material. High heritability along with high genetic advance observed for fruit length, yield, fruit girth and number of fruits per plant. Fruit yield had significant positive genotypic and phenotypic correlation with number of fruits per plant, fruit girth and vine length. Path coefficient analysis showed that fruit weight had maximum direct effect followed by number of fruits per plant. For selecting high yielding genotypes emphasis should be given on number of fruits per plant, fruit length, fruit weight, fruit girth and vine length.

A total of 135 cereal samples were collected from different districts of Karnataka state, India in which 69 samples were infected with Fusarium species. Among these 51 samples were having Fusarium verticillioides infection and among them 42 samples were positive for fumonisin production. Per cent incidence and frequency were high in maize samples with 33.12% and 47.54%, respectively followed by paddy and sorghum, while pearl millet was free from F. verticillioides infection. Relative density of F. verticillioides association was 59.50% among the screened samples. A total of 326 Fusarium species were isolated by screening 135 cereal samples and among these 194 isolates of F. verticillioides scored positive for VERTF-1 and VERTR species-specific pair of primers. Further amplification with VERTF-1 and VERTF-2 pair of primers recorded 176 isolates of fumonisin producing F. verticillioides. The study revealed high incidence, frequency and relative density of fumonisin producing F. verticillioides and production of fumonisins in cereals. It was amplified using one forward and two reverse primers to discriminate fumonisin producing from fumonisin non-producing F. verticillioides which stresses the need for the development of managemental stratergies before they enter into the food chain.

Fumonisin B1 (FB1), a mycotoxin produced by Fusarium species is a predominant Group 2B carcinogen occurring in maize and maize-based poultry feeds. It is shown to be nephrotoxic, hepatotoxic, neurotoxic, and immunosuppressing in animals. In this study, we report the ameliorating effects of a probiotic strain, Lactobacillus plantarum MYS6 on FB1-induced toxicity and oxidative damage in broilers. A 6-week dietary experiment consisting of 48 broilers was performed in six treatment groups. Probiotic treatment (109 cells/mL) involved pre-colonization of broilers with L. plantarum MYS6 while co-administration treatment involved supplementation of probiotic and FB1-contaminated diet (200 mg/Kg feed) simultaneously. At the end of the treatment period, growth performance, hematology, serum biochemistry, and markers of oxidative stress in serum and tissue homogenates were evaluated in all the broilers. The histopathological changes in hepatic and renal tissues were further studied. The results demonstrated that administration of L. plantarum MYS6 efficiently improved the feed intake, body weight and feed conversion ratio in broilers. It mitigated the altered levels of hematological indices such as complete blood count, hemoglobin, and hematocrit. Serum parameters such as serum glutamic oxaloacetic transaminase, serum glutamic pyruvic transaminase, creatinine, cholesterol, triglycerides, and albumin were significantly restored after administering the probiotic in FB1-intoxicated broilers. Additionally, L. plantarum MYS6 alleviated the levels of oxidative stress markers in serum and tissue homogenate of liver. The histopathological data of liver and kidney further substantiated the overall protection offered by L. plantarum MYS6 against FB1-induced cellular toxicity and organ damage in broilers. Our results indicated that co-administration of probiotic along with the toxin had better effect in detoxification compared to its pre-colonization in broilers. Collectively, our study signifies the protective role of L. plantarum MYS6 in ameliorating the FB1-induced toxicity in the vital organs and subsequent oxidative stress in broilers. The probiotic L. plantarum MYS6 can further be formulated into a functional feed owing to its anti-fumonisin attributes and role in mitigating FB1-induced hepatorenal damage.

Abstract Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer based Dual Convolutional Neural Networks is employed for detecting and classifying the osteoarthritis patients. The new Gaussian Aquila Optimizer (GAO) is devised to include Gaussian mutation at the exploitation stage of Aquila optimizer, which results in attaining the best global optimal value. Novel Dual Convolutional Neural Network (DCNN) is devised to balance the convolutional layers in each convolutional model and the weight and bias parameters of the new DCNN model are optimized using the developed GAO. The novelty of the proposed work lies in evolving a new optimizer, Gaussian Aquila Optimizer for parameter optimization of the devised DCNN model and the new DCNN model is structured to minimize the computational burden incurred in spite of it possessing dual layers but with minimal number of layers. The knee dataset comprises of total 2283 knee images, out of which 1267 are normal knee images and 1016 are the osteoarthritis images with an image of 512 × 512-pixel width and height respectively. The proposed novel GAO-DCNN system attains the classification results of 98.25% of sensitivity, 98.93% of specificity and 98.77% of classification accuracy for abnormal knee case–knee joint images. Experimental simulation results carried out confirms the superiority of the developed hybrid GAO-DCNN over the existing deep learning neural models form previous literature studies.