Development of functional cookies form wheat-pumpkin seed based composite flour.

To develop high quality cookies, even seemingly smallest changes depended on factors that can affect taste, texture, and nutritional value. In this light, this study aimed to investigate the upshot of refined wheat flour and pumpkin seed flour on properties of cookies such as antioxidant activity, thermal and oxidative stability. In view of the foregoing, the roasted pumpkin seeds of particle size below 500 µm were blended with wheat flour at different ratios (BR) to bake at selected pre-determined temperatures (T) and time durations (TD). The synergetic effect of aforesaid parameters on cookie development, BR, T, and TD was studied by varying the parameters between the range 6-15 %, 180-200 °C and from 8 to 12 min, respectively, for the baking process of cookies. Further, the process was modelled and scrutinized using numerical optimization to achieve a highly acceptable product. On that account, it was deduced that the optimal condition for BR, T, and TD were 12.87 %, 186 °C and 9.5 min, respectively, that could pave to beget the excellent quality cookies with overall acceptance score of 8, protein content 14.28 %, fat 17.85 %, ash 2.23 %, moisture 2.46 %, fiber 2.38 % and total color difference 12.01. The optimized cookies (OCs) were found to have higher protein (11.49-14.28 %), fiber (0.93-2.41 %), ash (2.19-1.77 %), total antioxidant activity (38.7158-43.1860 %), oxidative stability (28.61-51.24 h), Zn (1.42-2.63 mg/100g), and Fe (2.12-3.20 mg/100g) content as compared to the control. Laconically, the study results provided the optimized processing condition for developing high quality cookies with respect to improved nutritional value and comparable overall acceptability.

Characterization of teff straw from selected teff varieties from Ethiopia.

Utilization of biomass is important both for economic and environmental projection purposes. To use biomass for industrial applications as well as to reduce its pollution load on environment, it is important to characterize and determine the compositions of the biomass. In this work, the proximate and chemical analyses of straws of four (Dagim, Filagot, Kora and Kuncho) Teff (Eragrostis tef) varieties were investigated with three replications. The thermographic and FTIR of the teff straws and the ashes were also studied. The volatile matter contents of the teff straws were 78.80, 77.00, 80.20 and 80.60% for the Dagim, Kuncho, Kora and Filagot varieties, respectively. The ash contents of the straws were 6.34% for Dagim, Kuncho and Kora while the value is 6.00% for Filagot. The fixed carbon contents of the straws were 14.86, 16.67, 13.47 and 13.40% for Dagim, Kuncho, Kora and Filagot varieties, respectively. The silica contents of the teff straw for the Filagot, Kora, Dagim, and Kuncho varieties are 5.92, 5.66, 4.94, and 4.70%, respectively. This corresponds to 92.21, 91.59, 77.19 and 87.20% silica contents in the ashes produced from Filagot, Kora, Dagim, and Kuncho varieties, respectively. The results show that the proximate and chemical composition of ash produced from teff straws show slight differences. Moreover, the silica content of the teff straw is comparable with the values reported for rice husk and wheat straw. Thus, teff straw can be used for the production of silica.

Technological advancements in valorisation of industrial effluents employing hydrothermal liquefaction of biomass: Strategic innovations, barriers and perspectives.

Hydrothermal liquefaction (HTL) is identified as a promising thermochemical technique to recover biofuels and bioenergy from waste biomass containing low energy and high moisture content. The wastewater generated during the HTL process (HTWW) are rich in nutrients and organics. The release of the nutrients and organics enriched HTWW would not only contaminate the water bodies but also lead to the loss of valued bioenergy sources, especially in the present time of the energy crisis. Thus, biotechnological as well as physicochemical treatment of HTWW for simultaneous extraction of valuable resources along with reduction in polluting substances has gained significant attention in recent times. Therefore, the treatment of wastewater generated during the HTL of biomass for reduced environmental emission and possible bioenergy recovery is highlighted in this paper. Various technologies for treatment and valorisation of HTWW are reviewed, including anaerobic digestion, microbial fuel cells (MFC), microbial electrolysis cell (MEC), and supercritical water gasification (SCWG). This review paper illustrates that the characteristics of biomass play a pivotal role in the selection process of appropriate technology for the treatment of HTWW. Several HTWW treatment technologies are weighed in terms of their benefits and drawbacks and are thoroughly examined. The integration of these technologies is also discussed. Overall, this study suggests that integrating different methods, techno-economic analysis, and nutrient recovery approaches would be advantageous to researchers in finding way for maximising HTWW valorisation along with reduced environmental pollution.

Molecular Docking and Green Synthesis of Bioinorganic TiO2 Nanoparticles against E.coli and S.aureus.

This study used a simple solution evaporation approach to make a bioinorganic titanium dioxide (Bi-TiO2) photocatalyst for dye contaminant degradation. A variety of techniques, including X-ray diffraction (XRD), Fourier-transform infrared (FT-IR) spectroscopy, scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDAX), and differential reflectance spectroscopy, had been employed to classify the structural and optical properties of the prepared bioinorganic photocatalyst (UV-DRS). Using simulated solar irradiation, the photocatalytic activity of the produced Bi-TiO2 nanoparticles was examined by detecting the degradation of a solution of methylene blue (MB) as a model dye molecule. The developed Bi-TiO2 photocatalyst demonstrates superior photocatalytic action than commercially available powder TiO2, according to photo-degradation experiments. E.coli and S.aureus bacterial strains were employed to assess the antibacterial activity of Bi-TiO2 nanoparticles. The most active molecules that gain antibacterial activity were examined in isolated or extracted components from the tulsi plant. The chosen compounds were docked with thymidylate kinase (TMPK), a potential therapeutic goal for the preparation of novel antibacterial drugs with the PDB ID of 4QGG. Five compounds, namely rosmarinic acid, vicenin-2, orientin, vitexin, and isoorientin, out of the 27 chosen compounds, showed a higher docking score and may aid in boosting antibacterial activity. The synthesized Bi-TiO2 nanoparticles produced antibacterial activity that was effective against Gram-positive bacteria. The nanomaterials that have been synthesized have a lot of potential in wastewater treatment and biomedical management technologies.

Regional Language Speech Recognition from Bone-Conducted Speech Signals through Different Deep Learning Architectures.

Bone-conducted microphone (BCM) senses vibrations from bones in the skull during speech to electrical audio signal. When transmitting speech signals, bone-conduction microphones (BCMs) capture speech signals based on the vibrations of the speaker's skull and have better noise-resistance capabilities than standard air-conduction microphones (ACMs). BCMs have a different frequency response than ACMs because they only capture the low-frequency portion of speech signals. When we replace an ACM with a BCM, we may get satisfactory noise suppression results, but the speech quality and intelligibility may suffer due to the nature of the solid vibration. Mismatched BCM and ACM characteristics can also have an impact on ASR performance, and it is impossible to recreate a new ASR system using voice data from BCMs. The speech intelligibility of a BCM-conducted speech signal is determined by the location of the bone used to acquire the signal and accurately model phonemes of words. Deep learning techniques such as neural network have traditionally been used for speech recognition. However, neural networks have a high computational cost and are unable to model phonemes in signals. In this paper, the intelligibility of BCM signal speech was evaluated for different bone locations, namely the right ramus, larynx, and right mastoid. Listener and deep learning architectures such as CapsuleNet, UNet, and S-Net were used to acquire the BCM signal for Tamil words and evaluate speech intelligibility. As validated by the listener and deep learning architectures, the Larynx bone location improves speech intelligibility.

Synergistic Effect of Conventional Medicinal Herbs against Different Pharmacological Activity.

Triticum aestivum (Family: Poaceae), Ocimum sanctum (Family: Lamiaceae), and Tinospora cordifolia (Family: Menispermaceae) are commonly known as wheatgrass, tulsi, and giloy, respectively, which are the plants used as medicines for the treatment of various diseases. All three medicinal plants possess phenolic compounds with other important chemical constituents such as polysaccharides, aliphatic compounds, and alkaloids. The extract of these plants has been prepared and investigated for antioxidant, total phenolic content, total flavonoid content, and antimicrobial study in order to discover potential sources for new pharmaceutical formulations. To determine the antioxidant activity, a free radical scavenging assay for 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide was performed using ascorbic acid as the standard. The R 2 value of the prepared extract was found to be 0.9964 and 0.990 in DPPH and hydrogen peroxide scavenging activity, respectively. The phenolic and flavonoid content was found to be 87.50 µl/ml and 58.00 µl/ml, respectively. The diffusion method was used to screen the antimicrobial activity of the prepared extract sample against various microorganisms. This extract showed better results for antioxidant and antimicrobial activity.

Classification of Transgenic Mice by Retinal Imaging Using SVMS.

Alzheimer's disease is the neuro disorder which characterized by means of Amyloid- ß (A ß) in brain. However, accurate detection of this disease is a challenging task since the pathological issues of brain are complex in identification. In this paper, the changes associated with the retinal imaging for Alzheimer's disease are classified into two classes such as wild-type (WT) and transgenic mice model (TMM). For testing, optical coherence tomography (OCT) images are used to classify into two groups. The classification is implemented by support vector machines with the optimum kernel selection using a genetic algorithm. Among several kernel functions of SVM, the radial basis kernel function provides the better classification result. In order to deal with an effective classification using SVM, texture features of retinal images are extracted and selected. The overall accuracy reached 92% and 91% of precision for the classification of transgenic mice.

Severity Prediction over Parkinson's Disease Prediction by Using the Deep Brooke Inception Net Classifier.

Parkinson's disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain's dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson's Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson's disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson's disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection.

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing.

Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

Artificial Intelligence-Based Smart Comrade Robot for Elders Healthcare with Strait Rescue System.

A rising proportion of older people has more demand on services including hospitals, retirement homes, and assisted living facilities. Regaining control of this population's expectations will pose new difficulties for lawmakers, medical professionals, and the society at large. Smart technology can help older people to have independent and fulfilling lives while still living safely and securely in the community. In the last several decades, the number of sectors using robots has risen. Comrade robots have made their appearance in old human life, with the most recent notable appearance being in their care. The number of elderly individuals is increasing dramatically throughout the globe. The source of the story is the use of robots to help elderly people with day-to-day activities. Speech data and facial recognition model are done with AI model. Here, with the Comrade robotic model, elder people's healthcare system is designed with better analysis state. The aim is to put in place a simple robotic buddy to determine the health of the old person via a headband that has been given to them. Comrade robot may do things like senior citizens home automation, home equipment control, safety, and wellbeing sensing, and, in emergency situation, routine duties like navigating in the outside world. The fear that robotics and artificial intelligence would eventually eliminate most of the jobs is increasing. It is anticipated that, in order to survive and stay relevant in the constantly shifting environment of work, workers of the future will need to be creative and versatile and prepared to identify new business possibilities and change industry to meet challenges of the world. According to the research, reflective practice, time management, communicating, and collaboration are important in fostering creativity.

Implementation of Whale Optimization for Budding Healthiness of Fishes with Preprocessing Approach.

This article examines distinctive techniques for monitoring the condition of fishes in underwater and also provides tranquil procedures after catching the fishes. Once the fishes are hooked, two different techniques that are explicitly designed for smoking and drying are implemented for saving the time of fish suppliers. Existing methods do not focus on the optimization algorithms for solving this issue. When considering the optimization problem, the solution is adequate for any number of inputs at time t. For this combined new flanged technique, a precise system model has been designed and incorporated with a set of rules using contention protocols. In addition, the designed system is also instigated with a whale optimization algorithm that is having sufficient capability to test the different parameters of assimilated sensing devices using different sensors. Further to test the effectiveness of the proposed method, an online monitoring system has been presented that can monitor and in turn provides the consequences using a simulation model for better understanding. Moreover, after examining the simulation results under three different scenarios, it has been observed that the proposed method provides an enhancement in real-time monitoring systems for an average of 78%.

Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems.

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications.

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks. The model is designed in such a way that it minimizes the delays and complexity while authenticating the networks in 5G networks. The attacks are mitigated using an evolutionary model when it is trained with the relevant attack datasets, and the model is validated to mitigate the attacks. The simulation is conducted to test the efficacy of the model, and the results of simulation show that the proposed method is effective in improving the handling and authentication and mitigation against various types of attacks in mobile health applications.