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Classification and Alignment of SARS-Coronavirus Sequences: A Machine Learning and Bioinformatics Approach for Drug Discovery

Crop simulation models, Growth and development, Irrigation scheduling, Fertigation, Nitrogen dynamics, Product quality

Community water bodies inhabited by marine animals are used as a source of potable water by humans need to be tested for their quality. This work ensures that the quality of water is not compromised by calculating the Biological Pollution Index of water by means of an IoT system. Devices setup in each well sends parameter data using sensors, Arduino, and the cloud to an application monitored by the owner of said water body.

Genomic Sequence Similarity of SARS-CoV2 Nucleotide Sequences Using Biopython: Key for Finding Cure and Vaccines

As days rolls internet information grows exponentially and the personalized recommendation system has its vibrant area of research providing relevant information to the user needs. Hybrid recommender systems combines both implicit and explicit feedbacks from user by integrating collaborative and content based recommender system. However, the real time hybrid systems are not focusing on current temporal context of the user on providing the recommendation. This paper proposed a hybrid movie recommendation system that considers recent transactions and also demographic attributes for recommending a item to the target user. The demographic attributes aids in overcoming the cold start problem. The results of the experimental study on movie lens dataset clearly indicate that the proposed system was found to be effective by considering the recent transactions with higher ratings.

Due to the proliferation of COVID-19, the world is in a terrible condition and human life is at risk. The SARS-CoV-2 virus had a significant impact on public health, social issues, and financial issues. Thousands of individuals are infected on a regular basis in India, which is one of the populations most seriously impacted by the pandemic. Despite modern medical and technical technology, predicting the spread of the virus has been extremely difficult. Predictive models have been used by health systems such as hospitals, to get insight into the influence of COVID-19 on outbreaks and possible resources, by minimizing the dangers of transmission. As a result, the main focus of this research is on building a COVID-19 predictive analytic technique. In the Indian dataset, Prophet, ARIMA, and stacked LSTM-GRU models were employed to forecast the number of confirmed and active cases. State-of-the-art models such as the recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), linear regression, polynomial regression, autoregressive integrated moving average (ARIMA), and Prophet were used to compare the outcomes of the prediction. After predictive research, the stacked LSTM-GRU model forecast was found to be more consistent than existing models, with better prediction results. Although the stacked model necessitates a large dataset for training, it aids in creating a higher level of abstraction in the final results and the maximization of the model’s memory size. The GRU, on the other hand, assists in vanishing gradient resolution. The study findings reveal that the proposed stacked LSTM and GRU model outperforms all other models in terms of R square and RMSE and that the coupled stacked LSTM and GRU model outperforms all other models in terms of R square and RMSE. This forecasting aids in determining the future transmission paths of the virus.

The precedence of unexplored Uniform Resource Locators (URLs) is calculated in many existing works based on a linear combination of similarities of different texts of the web_page and the specified...

Augmented Reality (AR) is a trending technology that augments or superimposes an image generated by a computer system virtually into the real world environment for the user’s viewpoint using a smart phone or other hand held devices. AR shows recent advancements in the shopping domain with various implementation trails and refinement. The simplicity and flexibility in online shopping where people stay in their own place and do shopping brought a great challenge to retail shopping environment today. Retail stores are now struggling a lot to bring in the customers and the foot traffic has been greatly reduced due to which online sales are boosting and retail sales are stalling. This necessitates to bring new technological innovations to offline shopping to attract people. With the use of AR, it is possible to merge digital component to physical products in the store to stimulate the engagement of the shopping experience with more fun and joy. On the other hand, in the online shopping, though user reviews and product showcase aids the customers to analyze the quality, look and feel of diverse products, the buyer still cannot see how exactly the product fits in a real environment or how it works. Here plays AR a vital role in online shopping where it uses animations and visualization techniques to offer more value to their shoppers virtually aiding to see exactly the look of the product in user environment. This paper explains the advancement of AR in both retail and online shopping of various product domains with an implementation model of ShopAR for Online shopping and AR significance in near future.

Unauthorized service or product advertising messages sent via electronic mails are called as spam e-mails. Detecting spam e-mail remains a challenging task. Existing countermeasures based on the statistical keyword, conceptual and IP address-based blacklists are not efficient due to difficulty in finding new attack patterns generated by the Internet of Things botnet devices. The other spam detection approaches rely on a hybrid of conceptual knowledge engineering with machine learning techniques. But, modern spammers evade the hybrid techniques through word polysemy and word ambiguity due to the context-sensitive nature of words. In this paper, the integration of Naive Bayesian classification with conceptual and semantic similarity technique is proposed to combat the ambiguity raised through polysemy in spam detection. To analyse the effectiveness of our approach, the experiments were conducted on benchmark data sets such as Spambase, PU1, Enron corpus, and Ling-spam. From the experimental results, it is evident that our proposed system achieves high accuracy of 98.89% than the existing approaches.

The rapidly increasing volume of lightweight devices in Internet of Things (IoT) environment needs a strong Intrusion Detection System (IDS). Conventional IDS cannot be applied directly in IoT networks due to various communication architectures, standards, technologies, and environment specific services. The main problem with current IDS and handling techniques is that they can’t adapt to service changes in real-time. To overcome this open challenge, adaptive hybrid IDS based on timed automata controller approach is proposed in this paper. Proposed Hybrid IDS have additional knowledge in relation to frequent multimedia file formats and use this knowledge to carry out a comprehensive analysis of packets carrying multimedia files. Crowd sourcing online repository for signature based malicious pattern set generation is designed and self-tuning timed automaton is developed to detect the intruder in IoT networks. From the experimental results, it is evident that our proposed method, an adaptive hybrid IDS suit smart city applications and are accurate (99.06%) in detecting Denial of Service (DoS) attacks, control hijacking attacks, zero day attacks, and replay attacks in IoT environments.

The whole world is facing the dreadful burst of SARS-CoV-2 (COVID-19) and brought a severe threat among living society. COVID-19 is affecting human life as well as bringing a loss in terms of economic, finance, and several other resources. With the help of emerging technologies, machine learning methods like regression can be adopted to analyze this epidemic situation by considering the geographic distribution of cases. Regression allows us to predict the outcome using one or more predictor variables. Thus, this chapter analyzes COVID-19 with linear and polynomial regression with the goal of considering its everyday exponential behavior along with predicting COVID-19 future reachability across nations by using the real-time dataset.

Mobility is undergoing a notable change that will transform cities, businesses, and society. To plan a smart city which provides an efficient and impartial solution on civic mobility is a critical problem all over the world. Increasingly consumers are turning on-demand and app-based mobility services for the sequence of transportation options. This chapter explores designing a complete and efficient but secure public transport (PT) system, adapting to vehicle innovation and adoption, framing policies and approach to elevate adherence to air quality norms and other measures for better quality, building economic framework both physical and computerized to help inventive versatility arrangements, likewise creating open private organizations, and other institutional coordinated efforts to address the gridlock and other maintainable issues. Technology has the most vital role to play, by predicting the inflow and outflow data to handle transportation planning. Likewise, technology helps in developing the reliability of PT networks by giving details on arrivals, departures, and route information to voyagers for an issue-free journey. This type of management can help in proficient flow of vehicles.

With the rapid increase in the economy, the number of vehicles has grown extensively in the past few years. This leads to a sequence of traffic-related issues, starting with the driver’s and passenger’s safety, traffic congestion, and transportation delay. Internet of Vehicles (IoV) can provide a better solution for the above problems, by smartly connecting the vehicles. Thus, every vehicle senses and shares traffic-related information with other vehicles through the Internet. IoV provides smart and intelligent applications that consume a significant amount of computing power and network resources. However, the limited computational capacity of onboard ECUs makes it difficult to perform these highly demanding applications. Moreover, the data sensed from the vehicles are processed on the cloud data centers, which reside at multi-hop distance from the IoV devices. Thus, a longer period is required to pass the data between the IoV devices and applications resulting in degradation of application service delivery time and network congestion. This possesses a severe threat to the driver’s safety. To tackle these challenges, fog and edge computing can be used as an innovative technology that acts as an extension of the cloud at edge network. Edge computing provides processing of data at the Internet gateways, whereas fog computing includes gateways and additional computing components to reduce the burdens of resources and services from the user. In this paper, a detailed survey on fog and edge computing for smart and secure vehicular communications has been discussed. It also elaborates on the application management and mobility of fog computing nodes in vehicular environments.

Blockchain appears to be the most attractive in the future because of its distributed ledger technology and gain of popularity in industry and academics. Every peer in a blockchain network keeps the copy of a ledger and maintains consistency with the help of consensus algorithms. The advantage of using the blockchain is immutability, security, and transparency. Here in this paper, we have discussed Hyperledger framework and its tools in detail; this platform is hosted by Linux Foundation. Hyperledger is created to promote blockchain technologies. Hyperledger framework includes Fabric, Burrow, Indy, Grid, Iroha, Sawtooth, and various tools like Caliper, Cello, Composer, Explorer, Quilt, and Ursa. This framework and its tool can be used in various fields like creating any business models, creation of on-demand blockchain module, mobile applications, the building of supply chain solution, and many more. Hence, We have discussed several applications and challenges faced by blockchain technology beyond cryptocurrency.