Today computers are continuously betrothed in almost all domains and organizations. Thus, databases act as the heart for storing and retrieving information that contain huge digital data. However, in order to interact with such databases, it is necessary to have knowledge about the Structured Query Language (SQL), which is difficult for non-expert users to understand and manipulate. So, there is an emergent need to develop a smart and a user friendly computational technique to interact with databases. The current work proposed a smart technique that can handle such context. The proposed “Smart Data Retrieval Engine for Databases (SDRED)” provided an environment that allows a non-expert user to write and to execute the database queries easily. Furthermore, it retrieved the data stored in databases without a prior knowledge of the SQL. SDRED, which enables the non-expert user to write database queries in natural language (such as English) and to convert them to their SQL query equivalents. The current work presented a detailed design and evaluation for the proposed system by executing different database queries in English. The results established that SDRED successfully converted the non-expert user’s natural language queries into their equivalent SQL queries, thereby providing an easy and user-friendly environment to interact with databases.

The internet has grown in leaps and bounds and hence all the data is now available online; be it shopping, banking, private and public institutes or universities, private public sectors are all making their presence felt online. The online data is just a click away thanks to ubiquitous systems today. The browser does not require any specific program set up hence easier for the end user. Earlier the data online was static used HTML now it’s dynamic uses ASP, ASP.NET, Servlet, JSP and other operational tools therefore the internet operation is broken down into many categories. The problem arises with the customer while trying to buy something online. There are lots of online stores sometimes it’s difficult to browse through all products to get a better deal. The pricing of products are different on different sites, this is the first gap at the customer end. The second problem arises at the provider end. The second gap here is to understand the customer need.  How can the variation in prices be checked? ; The existing prices available on sites cannot be changed but the customer can be provided with options to select the best deal of the same product. For the first problem the paper deals with an API implementation wherein the information of at least some products is compared under one roof. How can the provider know the genuine customer? ; The second problem is resolved by the use of SVM. Last problem is in detecting if a customer visiting a site will actually buy the product being compared.

The paper focuses on the selection of ASP.NET to deal with the implementation problems stated and find solution to the forecasting problem using SVM. SVM and C4.5 are used for comparison.

A new form of wireless sensor networks is emerging as an important component of the Internet of Things (IoT) where camera devices are interconnected and endowed with an IP address to form visual sensor networks. The applications of these networks span from smart parking systems in smart cities, video surveillance for security systems to healthcare monitoring and many others which are emerging from niche areas. The management of such sensor networks will require meeting a higher quality of service (QoS) constraints than demanded from traditional sensor networks. While many works have focused only on energy efficiency as a way of providing QoS in sensor networks, we consider a novel modelling approach where local optimizations implemented on the sensor nodes are translated into pheromone distribution used in ant colony optimization for path computation. We propose a routing protocol called the multipath ant colony optimization (MACO) that finds QoS-aware routing paths for the sensor readings from source nodes to the sink by relying on four local parameters: the link cost, the remaining energy of neighboring nodes, sensor nodes location information and the amount of data a neighbor node is currently processing. Finally, we propose an architecture for integrating sensor data with the cloud computing. Simulation results reveal the relative efficiency of the newly proposed approach compared to selected related routing protocols in terms of several QoS metrics. These include the network energy efficiency, delay and throughput.

Parkinson disease that occurs at older ages is a neurological disorder that is one of the most painful, dangerous and non-curable diseases. One symptom that a person may have Parkinson’s disease is trouble that can occur in the voice of a person which is so-called dysphonia. In this study, an application based on assessing the importance of features was carried out by using multiple types of sound recordings dataset for diagnosis of Parkinson disease from voice disorders. The sub-datasets, which were obtained from these records and were divided into 70-30% training and testing data respectively, include the important features. According to the experimental results, the Random Forest and Logistic Regression algorithms were found successful in general. Besides, one or two of these algorithms were found to be more successful for each sound. For example, the Logistic Regression algorithm is more successful for the ‘a’ voice. The Artificial Neural Networks algorithm is more successful for the ‘o’ voice.

With the advent of artificial intelligence, the way technology can assist humans is completely revived. Ranging from finance and medicine to music, gaming, and various other domains, it has slowly become an intricate part of our lives. A neural network, a computer system modeled on the human brain, is one of the methods of implementing artificial intelligence. In this paper, we have implemented a recurrent neural network methodology based text generation system called Story Scrambler. Our system aims to generate a new story based on a series of inputted stories.  For new story generation, we have considered two possibilities with respect to nature of inputted stories. Firstly, we have considered the stories with different storyline and characters. Secondly, we have worked with different volumes of the same stories where the storyline is in context with each other and characters are also similar. Results generated by the system are analyzed based on parameters like grammar correctness, linkage of events, interest level and uniqueness.

Globally, the governments are concerned for saving human life but in recent days, the cost of medicine to save the human life grows rapidly, due to the different combination of the drugs sold by the drug store and based on the drugs availability in the drug store. As a regular routine, the Clinical physicians prescribe the drug name or druggist company name for the patient, instead of prescribing the brand name. For a particular drug name, more number of brand name both generic drugs and combination drugs were available. Almost all the physicians or druggist knows these generic drugs and combination drugs along with their dosage and recommends it for the patient. Since the patients are unaware about the brand name of the medicine, they are buying the costly drug without knowledge. At the same time the patients whoever provide the review on the drug consumed, mostly gives a negative review. On the other side, Government gives approval for every drug produced year by year and those drugs getting rejected for approval where sent for new alternative extension of chemical formula, as well government bans several combinations of drugs. There should be a database for awareness; it should help physicians, druggist and end user to look for banned and unbanned drug. We have identified that the drug details and their approvals were not maintained in the database. So, the proposed work is (i) to identify and classify the exact drug, which can be used as generic or combination and (ii) to create a banned drug model representation, which was unavailable. 

A hypergraph-based model named Drugs Relationship Discovery using Hypergraph (DRDH) was constructed as a preliminary design representation for drugs. Here, the drugs combination was considered as a hypervertex and the combination approved for manufacturing in the particular company was considered as an edge, having weight as the price of single dose usage based on Indian government listed drug. With this proposed model as a reference, the banned drugs are listed separately, manufacturing stopped drugs are highlighted with the weight of -1 and the physician or druggist can access the model to ensure the correct drug, their production, and usage. The Time complexity has been analyzed for the hypergraph, which was constructed based on the details of relationship identified among the drugs. Using the parameters of the list of drugs used in India on fever and the common cold, comparison over time taken to identify the relationship has also been done.

Mashups are an important way to allow normal users to build their own applications responding to the specific needs of each one. The basic components of mashups are Data and Web APIs especially Restful ones, but it is difficult for an unexperienced user to combine manually APIs with Data. Therefore, there is a need to predefine links between these resources to permit an easy combination. In this paper, we propose a new approach to make Restful Web APIs adhere to Linked Data principles, which facilitate their combination in mashup applications. The advantage of the proposed approach is the fact that it allows integrating linked data with the composition of Restful APIs, It also uses an algorithm to automatically create links between APIs.