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A simple chemical co-precipitation approach was used to produce in situ nanocomposites based on Mn-BIM, and the results were promising. It is possible to increase the photocatalytic degradation efficiency. The morphology of the nanocomposites is investigated using X-ray diffraction data and the structural features of the nanocomposites are investigated using electron microscopy. Mn-BIM is a nanoparticle with an average size of 50 nm. In order to determine their photo-catalytic activity, researchers investigated nanocomposites produced from industrial waste dyes rhodamine-B. Following 90 minutes in direct sunshine, the photo-degradation of these dyes occurs, with a photo-degradation rate of more than 90 percent. To evaluate whether nanocomposite materials have the ability to photodegrade dyes, tests have been carried out. The rate-limiting stepwise de-ethylation process for the degradation of rhodamine-B dye has been proposed as the mechanism of degradation.

In this work, a diagnostic application was performed by utilizing magnetic nanoparticles for the bio-sensing. A novel Fe3O4 nanostructure was synthesized in this paper using a simple hydrothermal method, the Fe3O4 nanoparticles are successfully controlled to provide a more dynamic site for catalytic reaction. FTIR-analysis, scanning electron microscopy (SEM), X-ray diffraction (XRD) was used to examine the morphology of the synthesized nanoparticles. The findings showed that a unique Fe3O4 nanostructure was obtained nanoparticles confined in nanosphere. The relative catalytic kinetics of Fe3O4 nanostructure has followed Michaelis–Menten behaviours, according to an analysis of peroxidase-like activity. An effective approach for colorimetric sensing of glucose was formulated on the bases of efficient peroxidase-mimicking activity of Fe3O4 nanoparticles. The synthesized Fe3O4 nanoparticles are very hopeful for the application of bio-sensors.

Mesoporous silica nanocomposite (MSNC) with a wall thick of around 10 nm were created using Fe3O4 nanoparticles as the inorganic template. In accordance with the results of SEM and BET analysis, MSNC were homogenous spherical particles with good dispersion, and their specific surface area it possible that Ibuprofen will become stuck within the MSNC carrier. Loading of drug shows a decline in a surface area from 225.08 to 69.25 m2 g-1, pore volume from 0.56 to 0.13cm g-1 and the pore diameter from 7.96 to 6.74 nm correspondingly. The amount of Ibuprofen entrapped in the carrier was measured by UV spectroscopy and total glycerol (TG) measurement, respectively. It was determined pore size distribution of MSNC changed before and after Ibuprofen entrapment. The release profile of Ibuprofen from MSNC was characterised by a three-stage pattern with an influence on the time between each stage.

Several areas in science and engineering have the relationships between their underlying data which can be represented as graphs, for example, molecular chemistry, node prediction, link prediction, computer vision, pattern recognition, social networking and more. In this article, an approach to a model which can handle such type of data is elaborated, which is Graph Neural Networks (GNN). GNN encompasses the neural network technique to process the data which is represented as graphs. Due to its massive success, GNN has made its way into many applications and is a popular architecture to work upon. This paper explains the graph neural networks, its area of applications and its day-to-day use in our daily lives. Some of the very common application is a social networking site which is on our hands regularly, and another could be the recommendation system which recommends us friends, or the products of our interest based on our pat choices and preferences. This paper also demonstrates the basic challenges encountered while implementing GNN. This paper will be a great help to those researchers who are keen to work in the domain of GNN.

New user recommendation is a big challenge for social networks site like reddit, where user profile is not so much rich. The aim of this paper is to enhance recommendation process that reddit can used for its users. We know that twitter and Facebook generate rich user information for social media behavior analysis and research. reddit recommendation system usually implement generic recommendation for its users in order to recommend subreddit, other side of coin, Like LinkedIn, amazon have very specialized user and item recommendation system based on user detail and create effective user profile for future reference. In this work fusion twitter and Facebook dataset user profile building by using tweets and post posted on Facebook by authentic unique user, and after that we extract user profile feature and parallelly perform subreddit listing for specific user feature generated by profile building. As per the reddit rank it is the 5th most visited site of USA and in world it holds 13th rank of most visited site. Twitter has 330 million active users with rich user profile like user demography. In this paper we analyze user timeline and identify user interest using machine learning and extract keywords using TF-IDF. Given the output with this machine learning used as input for a web based reddit subreddit listing application.

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.

Enterprise thrives on software applications that are built to fulfil the core business requirements. A single business application can offer a cluster of capabilities to generate value from processing huge amount of data often termed as Big Data. The time-based requirements of these applications are satisfied frequently by applying monolithic approaches with increased complexity and less scalability. Traditional approaches for Big Data Analytics suffer from overpriced, excessive and irrelevant data transfer owing to the constricted coupling amongst computing resources and data processing logic. Service-oriented approach came into existence as a new paradigm to enable applications to be rendered as service for better flexibility and scalability. Service orientation architecture avoids monolithic style but web services, one of its major implementation encourages monolith development of software application. Thus building a scalable, robust, resilient, cost-effective and optimum solution is one of the major requirements for outsized data. New software development style Microservices offer low degree of coupling and smaller size. This work reviews the existing and prevalent approaches like monolithic architecture in this area along with their drawbacks. This work also proposes a generic microservice model µBIGMSA for handling Knowledge Discovery in Big Data. Reference applications are implemented using proposed model. The effectiveness of the proposed model is evaluated by comparing the reference application with the monolithic application using various software metrics.

Aphids and beetles are known to be the most destructive house hold pests. The insecticides used against these pests are known to be toxic and harmful for plants, animals and human beings. Weeds are...

Objective: One of the best ways to counter any pathogen including SARS-CoV-2 is through enhancement of self-immune response Tetherin or BST2 is one such interferon-induced antiviral protein or immune regulatory protein of the human host that can restrict the release of virions of SARS-CoV-2 The ORF7a encoded protein of SARS-CoV-2 in-turn has been found to act as an antagonist to the human tetherin protein by interacting with key amino acid residues, thereby weakening the immune response Methods: In this research after consensus analysis of results from various servers mutations have been induced in selected residues of ORF7a coronavirus protein Most conserved residues have been obtained through Consurf server, ligand binding pockets, and active sites have been identified after combining results from multiple servers like CastP and 3DLigand Site Results: The most deleterious mutation has been identified through DUET and DynaMut servers and mutations induced thereafter have been tested for a decrease in binding affinity through the HawkDock server The protein-protein interaction studies have been performed before and after the induction of mutation through the HawkDock server Conclusions: The decrease in binding affinity between human tetherin and ORF7a protein is supportive of the need to study the induction of mutation and its detrimental effect on viruses, so that effective combat study can be designed with the interplay of our native immune system and drugs from external sources Moreover, other deterrents like anti-viral peptides can be designed against selected amino acids © IJCRR

During data transmission, a decentralised Mobile Ad Hoc Network (MANET) might result in high Energy Consumption (EC) and a short Network Lifetime (NLife). To address these difficulties, an on-demand Power and Load-Aware multipath node-disjoint source routing is presented based on the Enhanced Opportunistic Routing (PLAEOR) protocol. This unique protocol aims at using power, load, and latency to manage routing costs depending on control packet flooding from the destination node. However, the exchange of control packets from the target to all nodes may impact network efficiency. As a result, the PLAEOR is designed with a Multichannel Cooperative Neighbor Discovery (MCND) protocol to locate the nearby cooperative nodes for each node in the routing path during control packet transmission. Furthermore, when the packet rate of CBR is 20 packets/sec, the simulated results show that the PLAEOR-MCND achieves 120 sec of NLife and 20 J of EC than the state-of-the-art protocols.

Absorption and scattering of propagated microwave radio signals by atmospheric variables, particularly rainfall, remained a major cause of propagation attenuation losses and service quality degradation over terrestrial communication links. The International Telecommunications Union Radio (ITU-R) reports and other related works in the literature provided information on attenuation due to rain and microwave propagation data. Such propagation attenuation information in the tropical region of Nigeria is destitute, especially at lower radio waves transmission frequencies. Therefore, this study addresses this problem by employing 12-year rainfall datasets to conduct realistic prognostic modeling of rain rate intensity levels. A classification of the rainfall data into three subgroups based on the depth of rainfall in the region is presented. Additionally, an in-depth estimation of specific rain attenuation intensities based on the 12-year rainfall data at 3.5 GHz is demonstrated. On average, the three rainfall classes produced rain rates of about 29.27 mm/hr, 73.71 mm/hr, and 105.39 mm/hr. The respective attenuation values are 0.89 dB, 1.71 dB, and 2.13 dB for the vertical polarisation and 1.09 dB, 1.20 dB, and 2.78 dB for the horizontal polarisation at 0.01% time percentage computation. Generally, results indicate that higher rain attenuation of 12% is observed for the horizontal polarisation compared to the vertical polarisation. These results can provide valuable first-hand information for microwave radio frequency planning in making appropriate decisions on attenuation levels due to different rainfall depths, especially for lower frequency arrays.

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.

Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

Background: Myocardial infarction, or heart attack, is caused by a blockage of a coronary artery, which prevents blood and oxygen from accessing the heart properly. Arrhythmias are a form of CVD that refers to irregular variations in the normal heart rhythm, such as the heart beating too quickly or too slowly. Arrhythmias include Atrial Fibrillation(AF),Premature Ventricular Contraction(PVC), Ventricular Fibrillation(VF), and Tachycardia are just a few examples of arrhythmias. It aggravates if not detected and treated on time i.e., on-time /proper diagnosis of arrhythmias may minimize the risk of death. It is very labor-intensive to externally evaluate ECG signals, due to their small amplitude. Furthermore, the analysis of ECG signals is arbitrary and can differ between experts. As a consequence, a computer-aided diagnostic device that is more objective and reliable is needed. Methods: In the recent era, Machine Learning based approaches to detect arrhythmias has been established proficiently. In this view, we proposed a hybrid Deep Learning-based model to detect three types of arrhythmias on MIT-BIH arrhythmia databases. In particular, this paper makes two-fold contributions. First, we translated 1D ECG signals into 2D Scalogram images. When one-dimensional ECG signals are turned into two-dimensional ECG images, noise filtering and feature extraction are no longer necessary. This is notable since certain ECG beats are ignored by noise filtering and feature extraction. Then, based on experimental evidence, we suggest combining two models, 2D-CNN-LSTM, to detect three forms of arrhythmias: Cardiac Arrhythmias (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). Results: The experimental findings indicate that the model attained 99\% accuracy for "normal sinus rhythm," 100\% accuracy for "cardiac arrhythmias," and 99\% accuracy for "congestive heart failures," with an overall classification accuracy of 98.6\%. The sensitivity and specificity were 98.33\% and 98.35\%, respectively. The proposed model, in particular, will aid doctors in correctly detecting arrhythmia during routine ECG screening. Conclusion: As compared to the other State-of-the-art methods our proposed model outperformed and will greatly minimise the amount of intervention required by doctors.

Air pollution is one of the major problems being faced by the world today. Globally, air pollution causes more than 10 million deaths annually either directly or indirectly. The efforts to fight air pollution rely very heavily on the data that is collected from the surface sensors. Any strategy to predict or counter the effects of specific pollutants is affected by the fact that the data collected is often mixed with impurities in the form of outliers. The outliers need to be detected and removed before the data can be used for decision making. The methods used for outlier detection are limited in their accuracy and are often plagued by problems of false positives, which are readings that are falsely identified as outliers, but which in reality are novelty readings. We propose a two-step method to detect the outliers that reduces instances of false positives by differentiating true outliers from circumstantial outliers. This would help in effective prediction of air pollution or pollutant levels thereby assisting in devising effective countermeasures.

The havoc created by Corona virus has been dealt with using various integrative approaches adopted by laboratories through-out the world. Use of anti-viral peptides (AVPs) although new but has shown tremendous potential against many pathogens. Previously AVPs have been designed against spike protein of corona virus which is the major entry mediating molecule. Using various in-silico strategies, in this research work AVPs have been modeled against lesser studied viral proteins namely ORF7a protein, Envelope protein (E), Nucleoprotein (N), and Non-Structural protein (Nsp1 and Nsp2). The predicted AVPs have been docked against various host as well as viral proteins. The interaction of small AVPs seems capable of interfering with binding between viral protein and its host counterpart. Therefore, these AVPs can act as a deterrent against novel corona virus, which requires further validation through laboratory techniques.

Air pollution is one of the greatest problems being faced by mankind. Millions of people die each year because of reasons directly or indirectly related to air pollution. Effective strategies to counter the harmful effects of air pollution are an imperative need of the times. The responses to the air pollution problems are usually knee-jerk reactions, which don’t help in the long run. For developing an effective counter-strategy for combating air pollution, it is necessary to focus the efforts on the pollutants that are most responsible for the air pollution. This paper focuses on identifying the pollutant that plays the most important role in defining the Air Quality Index of a region and also attempts to establish the trend patterns followed by the identified pollutant. This will help the decision makers to devise counter-strategies well in advance for countering the harmful effects of the main pollutant thereby helping to reduce air pollution.

Data analytics is one of the most important fields in this computing world. Every emerging paradigm finally results in the generation of data. The rapidly growing attraction from different industries, markets, and even academics results in the requirement of deep study of big data analytics. The proposed work deals with the unstructured data and raw data, and it then converts that into the structured and consistent data by applying various modern methods of the data warehouse. These methods include logistics and supply chain management (LSCM), customer relationship model (CRM), and business intelligence for market analysis to make fruitful decisions for an organization. Thus, the processes of data analysis are performed in data warehouses that are ETL process which describes the steps of gathering and transforming the data and finally placing the data to the destination. The proposed CHAIN method is a core sector of our research work as a naive approach assisting in improvising the market power. Here, in this research work, a market analysis on an IT hardware sector is performed that deals with the sales of peripherals and telecommunication devices in the market. This can be achieved via continuous communication of clients and retailers to generate meaningful and relevant data, and this data can also be analyzed for generating various required reports.

Air pollution is one of the biggest problems being faced by the world today and effective measures are being sought to counter this menace. In order to devise a proper strategy of countermeasures, it is imperative to ensure that the data on which the modeling and decision making is being based is clean. Outliers usually creep in the sensor generated data due to human and system errors and have a degrading effect on the decision making. A number of methods have been tested for detection of outliers but an incremental approach is highly sought after as the temporal air quality data is available as a data stream. We have proposed a framework to analyze the performance of statistical outlier detection methods as being the answer to the continuous analysis problem. We have presented a comparative analysis of five statistical methods viz. Z-score, InterQuartile Range, Grubb’s Test, Hampel’s test, Tietjen-Moore Testfor outlier detection when used over the complete dataset and when used in an incremental mode.

The suicide tendency is increasing day to day. It is having a negative impact on our youth. Human at possibility of suicide does not want help before trying to attempt and do not feel necessity to any mental health counselling. As a result, suicidal tendency is becoming social challenge. At the same time, social media are becoming popular for communication and exchange emotional expression to the world. Social media like Twitter, Facebook and Instagram play major role for emotion sharing. Therefore, huge number of people publishes their emotion of depression and happiness within notes in these social media like Twitter. Social platform like Twitter has a large number of collection of emotional notes. In this situation, machine learning helps in early prediction of the depression and suicidal tendency. Therefore, in this paper, we develop a soft solution which is able to early detect suicidal tendency among the youth. In this approach, we train a model with genuine suicidal notes and post collected from different sources and generate score for input social media tweet high or low for social tendency prediction. With the help of social media postings like Twitter, we are able to identify risk of suicide.

In last few years, information world has come across one of the most appealing paradigms, namely Internet-of-Things (IoT). Both the industry as well as the academia is fascinated by the open issues and research challenges of Internet-of-Things. The enormity in the development, expansion, and advantages of the paradigm is unbelievable, and this results in the transition of the information world to this paradigm. At the same time, it raises the security concerns along with many other open issues towards the paradigm. This paper specifically focuses on the confidentiality component out of the three components of security triad, namely confidentiality, integrity and availability. Although researchers have high interest towards all the three components, but the questions posed by many researchers, academicians and end users raise major issues over ‘confidentiality’. Confidentiality is all about guaranteeing the authentic access to a piece of information or a service or even a resource, to a particular individual or computing device. The resource in question may be one of the resources of an IoT system. This can be achieved by deploying some authentication techniques. Here we discuss various types of authentication techniques, purposely putting our emphasis on one of the well-known authentication technique, viz. passwords. This work reviews the existing techniques, their drawbacks and claimed advantages of the upcoming password techniques. It also surveys some of the supportive methods for the naive users of password techniques. This paper aims at classifying, comparing and encapsulating the problems, demanded solutions and suggestions from relevant published technical and review articles in the field of authentication. This paper particularly highlights graphical password schemes. This proposes a naive graphical password scheme to assure the authentic access of IoT resources, namely TCpC. At the end we have mentioned the advantages of TCpC over other authentication schemes. We have also compared and analyzed the different proposed password schemes with our technique.

Researchers shows a huge interest in Latent Class Analysis (LCA) in various domains over the last two decades. We proposed a new Latent Class Data Analysis using Statistical modeling approach to categorize better and worst Hotel to Stay. The main objective of this study was to find the unobserved classes in the Trap Advisor dataset. The results allow to identify new entry of the Hotel and detects whether it lies in Good Hotel category or in worst Hotel category. For evaluation and demonstration purpose freely, available Trip Advisor dataset is used.

Nowadays an important issue as well as challenge in data mining is obviously is outlier detection. Outlier detection has been used in many areas such as Fraud detection, Intrusion detection, Health care, Fault detection, etc., where detection of outliers is based on the different characteristics of data or datasets. In this current age of ‘Information Technology’, large numbers of processes are obtainable in the domain of data mining to discover the outliers by successfully creating the clusters and after that detecting the outliers from these created clusters. In data mining, cluster methods are highly essential and have been applied from micro- to macro-applications. Basically clusters are a pool of similar data objects put together grounded on the attributes and district features they have. Specifically outlier detection is used to recognize and exclude inconsistency from the available data sets. In the presented work an algorithm has been suggested which is based on clustering approach to the given data sets. The proposed algorithm efficiently detects outliers inside the clusters by using clustering algorithm and weight based approach.