Comparative studies on inducers in the production of naringinase from Aspergillus niger MTCC 1344

This research provides detailed systematic study of the effect of different inducers (hesperidin, naringenin, naringin, rhamnose and rutin) in naringinase production by Aspergillus niger MTCC 1344. Cultures were carried out in shake flasks and they produce extracellular naringinase in a complex (molasses, peptone and salts) medium. The optimized concentration (%) of naringin, rhamnose, naringenin, rutin and hesperidin for   maximized naringinase production are 0.1, 0.375, 0.01, 0.2 and 0.2, respectively. Compared with control,  inducers increased the naringinase production by many folds in the order of naringin (6.63) > rhamnose (4.87) > naringenin (3.26) > rutin (2.84) > hesperidin (2.35). Under optimum conditions, about 9.68 units of enzyme per ml complex medium containing naringin were obtained on the 7th day. The activity to inducer (A/I) ratio was 968 Ug-1 naringin, and the cultivation time was shorter in submerged production. The results indicate that naringinase activity used naringin as an inducer which was significantly higher than the other four inducers. Therefore naringin is recommended for naringinase production.Key words: Naringin, naringenin, rutin, hesperidin, rhamnose, naringinase, Aspergillus, inducer, molasses

Ontology Based Public Healthcare System in Internet of Things (IoT)

AbstractInternet of Things is a growing technology that is predicted to discover new drugs and medical treatments. The efficiency and quality of healthcare have high potential features as flexibility, adaptability, affinity, cost shrinkage, and high speed. This technology helps us to understand the specific risks related to security and privacy. This paper targets on a Healthcare information system based on ontology method. In particular, security and privacy challenges are analyzed in the proposed Ontology-based healthcare information system. Emergency medical services (EMS) are a type of emergency service dedicated to providing out-of-hospital acute medical care, transport to definitive care. Moreover, a functional infrastructure plan is provided to exhibit the unification between the proposed application architecture with the Internet of Things and ontology hierarchy

Cross sectional study evaluating the correlation of thyroid dysfunction with severity of disease in rheumatoid arthritis

Background: The present study was conducted to evaluate the correlation of disease severity in RA and thyroid dysfunction.Methods: The present cross-sectional descriptive study enrolled 164 participants aged 12 years and above diagnosed as having RA. Use of drugs causing thyroid dysfunction, malignancy, diabetes mellitus, systemic hypertension, pregnancy and prior thyroidectomy were the criteria for exclusion. Data was analyzed using R and tests of significance were Chi square test and independent sample t-test and Pearson correlation. Institutional ethics committee approved the study and written informed consent was obtained from all study participants.Results: Serum TSH positively correlated with DAS 28 (r=0.2, p=0.005), ESR (r=0.2, p=0.03), CRP (r=0.2, p=0.006), RA factor (r=0.2, p=0.003), subjective assessment (r=0.3, p= 0.001) and anti TPO antibodies (r=0.7, p=0.001). Free T4 negatively correlated with DAS28 (r=-0.2, p=0.006), ESR (r=-0.2, p=0.02), CRP (r=-0.2, p=0.01). RA factor (r=-0.2, p=0.01), subjective assessment (r=-0.2, p= 0.01), anti TPO (r=-0.6, p=0.001) and Free T3 negatively correlated with DAS28 score (r=-0.2, p=0.02) , ESR (r=-0.2, p=0.03), RA factor (r=-0.3, p=0.001) and anti TPO antibodies (r=- 0.3, p=0.001).Conclusions: Hypothyroidism was significantly associated with disease severity of RA with linear positive correlation of TSH with DAS28 score, ESR, CRP, RA factor, subjective assessment and anti TPO antibodies, linear negative correlation of serum free T4 with DAS 28 score, ESR, CRP, RA factor, subjective assessment and anti TPO antibody and linear negative correlation of free T3 with DAS28 score, ESR, RA factor and anti TPO antibody was observed

Hybrid Sine-Cosine Black Widow Spider Optimization based Route Selection Protocol for Multihop Communication in IoT Assisted WSN

In the modern era, Internet of Things (IoT) has been a popular research topic and it focuses on interconnecting numerous sensor-based devices primarily for tracking applications and collecting data. Wireless Sensor Networks (WSN) becomes a significant element in IoT platforms since its inception and turns out to be the most ideal platform for deploying various smart city application zones namely disaster management, home automation, intelligent transportation, smart buildings, and other IoT-enabled applications. Clustering techniques were commonly used energy-efficient methods with the main purpose that is to balance the energy between Sensor Nodes (SN). Routing and clustering are Non-Polynomial (NP) hard issues where bio-inspired approaches were used for a known time to solve these issues. This study introduces a Hybrid Sine-Cosine Black Widow Spider Optimization based Route Selection Protocol (HSBWSO-RSP) for Mulithop Communication in IoT assisted WSN. The presented HSBWSO-RSP technique aims to properly determine the routes to destination for multihop communication. Moreover, the HSBWSO-RSP approach enables the integration of variance perturbation mechanism into the traditional BWSO algorithm. Furthermore, the selection of routes takes place by a fitness function comprising Residual Energy (RE) and distance (DIST). The experimental result analysis of the HSBWSO-RSP technique is tested using a series of experimentations and the results are studied under different measures. The proposed methodology achieves 100% packet delivery ratio, no packet loss and 2.33 secs end to end delay. The comparison study revealed the betterment of the HSBWSO-RSP technique over existing routing techniques

Integrating Ergonomic Factors with Waste Identification Diagram to Enhance Operator Performance and Productivity in the Textile Industry

Industries have introduced lean manufacturing systems to outperform their competitors and sustain their growth. The implementation of lean tools results in satisfying the customer needs. Industries focus primarily on technical assistance when implementing lean strategies, but the success and sustainability of a lean strategy largely depend on the skill and cooperation of the workers. Research findings show that most industries have not attached importance to human factors while implementing lean logic. The negligence of human factors affects the quality of life of workers. Hence, this study intends to improve the quality of life and efficiency of workers by integrating ergonomic factors with the implementation of lean strategies in apparel industry. To accomplish this, the waste identification diagram was improved by adding a component to determine operators’ performance and analyse human factors. The ergonomic-waste identification diagram has been created to identify tasks related to ergonomic investigations and analyse human factors with lean metrics. The results point to the fact that an egalitarian approach increases the performance of operators and productivity of the organization

Convolutional Neural Networks for Medical Image Diagnosis and Prognosis

One of the most incredible machine learning methods is deep learning. Utilised for picture categorization, clinical archiving, item identification, and other purposes. The quantity of medical image archives is expanding at an alarming rate as hospitals employ digital photos for documentation more frequently. Digital imaging is essential for assessing the severity of a patient's illness. Medical imaging has a wide variety of uses in research and diagnostics. Due to recent developments in image processing technology, self-operating identification of medical photos is still a research area for computer vision researchers. We require an appropriate classifier in order to categorise medical photos using various classifiers. After organ prediction and classification, the research was modified to include medical picture recognition. For medical picture detection, pretrained convolutional networks and Kmean clustering techniques similar to those used for organ identification are employed. Separating the training from the test data allowed for the data's authentication. The application of this strategy has been proven to be most effective for categorising various medical images of human organs

Clustering Techniques for Recommendation of Movies

A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated

Barnacles Mating Optimizer with Hopfield Neural Network Based Intrusion Detection in Internet of Things Environment

Owing to the development and expansion of energy-aware sensing devices and autonomous and intelligent systems, the Internet of Things (IoT) has gained remarkable growth and found uses in several day-to-day applications. Currently, the Internet of Things (IoT) network is gradually developing ubiquitous connectivity amongst distinct new applications namely smart homes, smart grids, smart cities, and several others. The developing network of smart devices and objects allows people to make smart decisions with machine to machine (M2M) communications. One of the real-world security and IoT-related challenges was vulnerable to distinct attacks which poses several security and privacy challenges. Thus, an IoT provides effective and efficient solutions. An Intrusion Detection System (IDS) is a solution for addressing security and privacy challenges with identifying distinct IoT attacks. This study develops a new Barnacles Mating Optimizer with Hopfield Neural Network based Intrusion Detection (BMOHNN-ID) in IoT environment. The presented BMOHNN-ID technique majorly concentrates on the detection and classification of intrusions from IoT environments. In order to attain this, the BMOHNN-ID technique primarily pre-processes the input data for transforming it into a compatible format. Next, the HNN model was employed for the effectual recognition and classification of intrusions from IoT environments. Moreover, the BMO technique was exploited to optimally modify the parameters related to the HNN model. When a list of possible susceptibilities of every device is ordered, every device is profiled utilizing data related to every device. It comprises routing data, the reported hostname, network flow, and topology. This data was offered to the external modules for digesting the data via REST API model. The experimental values assured that the BMOHNN-ID model has gained effectual intrusion classification performance over the other models