An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.

Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

Characterization of Neem (Azadirachta indica A. Juss) seed volatile compounds obtained by supercritical carbon dioxide process.

Extraction process employing Supercritical fluid carbon dioxide (SCF) yields bioactive compounds near natural forms without any artifact formation. Neem seed was subjected to SCF at different temperatures and pressure conditions. These extracts were partitioned to separate volatile fraction and were analyzed by Gas Chromatography-Mass spectroscopy along with the volatiles extracted by the hydro-distillation method. Experimental results show that there is a significant effect of pressure and temperature on isolation of a number of volatile compounds as well as retention of biologically active compounds. Twenty-five volatile compounds were isolated in the Hydro-distillate compare to the SCF extract of 100 bar, 40 °C which showed forty volatile compounds corresponds to 76.38 and 92.39% of total volatiles respectively. The majority of bioactive compounds such as Terpinen-4-ol, 1,2,4-Trithiolane, 3,5-diethyl, allyl isopropyl sulphide, Cycloisolongifolene, á-Bisabolene, (-)-α-Panasinsen, Isocaryophyllene, trans-Sesquisabinene hydrate, 1-Naphthalenol, were identified in the extract when isolated at 100 bar and 40 °C.

Effect of edible coating on the aromatic attributes of roasted coffee beans.

Coffee is known throughout the world for its distinct aroma and flavour which results from a number of volatile compounds present in it. It is very difficult to arrest the aromatic compounds once the roasting process is complete and it becomes even more challenging to store the beans for a longer time with the retained volatiles as these compounds are easily lost during industrialized processing such as the grinding of roasted coffee beans and storage of ground coffee. Thus, an attempt was made to minimise the loss of volatile from roasted coffee beans by coating with Carboxymethyl cellulose (CMC), Hydroxypropylmethyl cellulose (HPMC) and Whey protein concentrate. Coffee volatiles were analysed by Gas chromatography and 14 major compounds were identified and compared in this study. Results showed an increase in the relative area of major volatile compounds in coated roasted coffee beans when compared with unroasted coffee beans for consecutive two months. Moreover, effect of coating on textural properties and non-volatiles were also analysed. The results have indicated that edible coatings preserve the sensory properties of roasted coffee beans for a longer shelf life and cellulose derivatives, as an edible coating, exhibited the best protecting effect on roasted coffee beans.

Physicochemical characteristics of green coffee: comparison of graded and defective beans.

Defective (triage) coffee beans are beans rejected after separating the graded ones according to the size and color. These coffee beans represent about 15% to 20% of coffee production in India but are not utilized for beverages since these affect the quality of coffee brew. In the present study, physical characteristics such as bean density, brightness, titratable acidity, pH, moisture, and total soluble solids and also chemical composition, namely, caffeine, chlorogenic acids, lipids, sucrose, total polyphenols, and proteins, were evaluated in defective as well as in graded green coffee beans. The physical parameters such as weight, density, and brightness of defective coffee beans were low compared to the graded beans, which is due to the presence of immature, broken, bleached, and black beans. Caffeine content was low in triage beans compared to graded beans. Chlorogenic acids, one of the composition in coffee responsible for antioxidant activity, was found to be intact (marginally high in some cases) in defective coffee beans. Hence, triage coffee beans can be evaluated as a source of antioxidant or radical scavenging conserve for food systems.

Caffeine in coffee: its removal. Why and how?

The popularity of coffee as a beverage is ever increasing despite the fact that there are reports antagonized to its consumption. Of the several factors cited, the alkaloid caffeine present in coffee can cause addiction and stimulate the central nervous system. It has an effect on the cardiovascular system with a slight increase in blood pressure and heart output. It undergoes biotransformation in the human body to form methylated derivatives of uric acid. In recent times, much effort has gone into the research on the removal of caffeine in coffee, resulting in a specialty product called decaffeinated coffee. Decaffeination methods mainly employ organic solvents or water or supercritical carbon dioxide. These methods with their attendant advantages and disadvantages are reviewed in this article.