Recent Patents on Computer Science - Volume 12, Issue 1, 2019

Background: Essential proteins are significant for drug design, cell development, and for living organism survival. A different method has been developed to predict essential proteins by using topological feature, and biological features. Objective: Still it is a challenging task to predict essential proteins effectively and timely, as the availability of protein protein interaction data depends on network correctness. Methods: In the proposed solution, two approaches Mean Weighted Average and Recursive Feature Elimination is been used to predict essential proteins and compared to select the best one. In Mean Weighted Average consecutive slot data to be taken into aggregated count, to get the nearest value which considered as prescription for the best proteins for the slot, where as in Recursive Feature Elimination method whole data is spilt into different slots and essential protein for each slot is determined. Results: The result shows that the accuracy using Recursive Feature Elimination is at-least nine percentages superior when compared to Mean Weighted Average and Betweenness centrality. Conclusion: Essential proteins are made of genes which are essential for living being survival and drug design. Different approaches have been proposed to anticipate essential proteins using either experimental or computation methods. The experimental result show that the proposed work performs better than other approaches.

Background: Health is the major concern of each and every individual. Being fit both physically and mentally is not an easy task. Yoga and meditation is considered as an optimal solution for the same. In Yoga, Yogi (person who does yoga) performs various asanas (body postures) which energize and refresh their body cells and keep them fit. The real purpose of yoga asanas and breathing techniques is to achieve optimal health possibly the best physical condition based on their life style, environment, age and genetics. Various clinical studies claim that yoga can provide improved mental and physical fitness rather than other physical training or stress management techniques. Objective: Our aim is to increase the performance of the postures of the Yogis, through yoga assistant kit with prediction intelligence which will assist the person to perform suitable yoga postures. This will help the Yogis to achieve more positive results in the practice of Yoga, with highest quality of meditation. The developed IoT kit consists of a hardware module (embedded in wrist band) and a mobile application. The yogi should wear the wrist band while practising yoga. The wrist band consists of various sensors like temperature sensor, pressure sensor, humidity sensor etc. which sense body parameters and store it in a central database. Using neural networks and embedded intelligence our system aims to predict the number of sun salutations a person (yogi) should perform based on the parameters collected from the kit. The results showed that our system works as a virtual trainer which suggests the yogi with the appropriate asanas to be performed based on present body conditions. Methods: It is safe to wear this light weight wrist band as it is made up of a cotton band. The components are embedded inside the band and is safe to use though it uses button cells as a power source. The system is charged by button cells. It is both economical and safe to use it as the kit is designed in such a manner that it doesn't cause any sort of skin allergies or side effects. Discussion: There is no standard yoga assistant kit available in the market as of now. So our proposed kit can assist the yoga performers to perform yoga in an efficient manner. The intention of our kit is not to improve the health of a yoga person instead it focuses on assisting the yoga person with a set of asanas to be performed at a particular body condition. The smart phone version provides live assistance for the yoga performer with relevant videos. The kit doesn't consist of any expensive components and hence we can market this product in a nominal price. We performed a clinical study in Amrutha Yoga centre and the results showed that it is non allergic and safe to use for both kids and elder persons. Conclusion: Thus our proposed yoga kit will be an intelligent assistant for every yoga performer to practice yoga efficiently and effectively.

Background: A physical object, which is actually in 3D form, is captured by a sensor/ camera (in case of computer vision) and seen by a human eye (in case of a human vision). When someone is observing something, many other things are also involved there which make it more challenging to recognize. After capturing such a thing by a camera or sensor, a digital image is formed which is nothing other than a bunch of pixels. It is becoming important to know that how a computer understands images. Objective: This paper is for highlighting novel techniques on 3D object recognition system with local shape descriptors and depth data analysis. Methods: The proposed work is applied to RGBD and COIL-100 datasets and this is of four-fold as preprocessing, feature generation, dimensionality reduction, and classification. The first stage of preprocessing is smoothing by 2D median filtering on the depth (Z-value) and registration by orientation correction on 3D object data. The next stage is of feature generation and having two phases of shape map generation with shape index map and SIFT/SURF descriptors. The dimensionality reduction is the third stage of this proposed work where linear discriminant analysis and principal component analysis are used. The final stage is fused on classification. Results: Here, calculation of the discriminative subspace for the training set, testing of object data and classification is done by comparing target and query data with different aspects for finding proper matching tasks. Conclusion: This concludes with new proposed approach of 3D Object Recognition. The local shape descriptors are used for 3D object recognition system to implement and test. This system is achieves 89.2% accuracy for Columbia object image library-100 images by using local shape descriptors.

Background: Recent advances in the field of information and social network has led to the problem of community detection that has got much attention among the researchers. Objective: This paper focus on community discovery, a fundamental task in network analysis by balancing both attribute and structural similarity. The attribute similarity is evaluated using the Jaccard coefficient and Structural similarity is achieved through modularity. Methods: The proposed algorithm is designed for identifying communities in social networks by fusing attribute and structural similarity. The algorithm retains the node which has high influence on the other nodes within the neighbourhood and subsequently groups the objects based on the similarity of the information among the nodes. The extensive analysis is performed on real world datasets like Facebook, DBLP, Twitter and Flickr with different sizes that demonstrates the effectiveness and efficiency of the proposed algorithm over the other algorithms. Results: The results depicts that the generated clusters have a good balance between the structural and attribute with high intracluster similarity and less intracluster similarity. The algorithm helps to achieve faster runtime for moderately-sized datasets and better runtime for large datasets with superior clustering quality.

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items' list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user's interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

Background: Major Depressive Disorder (MDD) in simple terms is a psychiatric disorder which may be indicated by having mood disturbances which are consistent for more than a few weeks. It is considered a serious threat to psychophysiology which when left undiagnosed may even lead to the death of the victim so it is more important to have an effective predictive model. The major Depressive disorder is often termed as comorbid medical condition (medical condition that co-occurs with another), it is hardly possible for the physicians to predict that the victim is under depression, timely diagnosis of MDD may help in avoiding other comorbidities. Machine learning is a branch of artificial intelligence which makes the system capable of learning from the past and with that experience improves the future results even without programming explicitly. As in recent days because of the high dimensionality of features, the accuracy of the predictions is comparatively low. In order to get rid of redundant and unrelated features from the data and improve the accuracy, relevant features must be selected using effective feature selection methods. Objective: This study aims to develop a predictive model for diagnosing the Major Depressive Disorder among the IT professionals by reducing the feature dimension using feature selection techniques and evaluate them by implementing three machine learning classifiers such as Naïve Bayes, Support Vector Machines and Decision Tree. Method: We have used Random Forest based Recursive Feature Elimination technique to reduce the feature dimensions. Results: The results show a considerable increase in prediction accuracy after applying feature selection technique. Conclusion: From the results, it is implied that the classification algorithms perform better after reducing the feature dimensions.

Background: Comparing with the actual progress of the project and the expected target in a project management, the processes are generally inadequate due to the overall market situation and internal factors. If the progress and quality of the project cannot meet the requirements, then the processes need to be adjusted to improve the project efficiency. In the related patents of project progress management, they seldom consider the recognition of important processes. Methods: In this paper, based on Bayesian Network (BN), a new method for project progress management is provided by systematic modeling methods, reasoning methods and identification methods of importance processes. Results: Based on the importance analysis of process nodes and BN reasoning, the key processes of the project progress and the important influencing nodes of the process are identified. Conclusion: According to the results of process importance analysis, the allocation object can be got when adding the resources of the project. These are helpful to improve the operational efficiency of the project progress management and provide effective methods to identify important processes.

Background: Systems whose requirements change at a rate that necessitates adaptation without human intervention are called self-adaptive systems, and they have the ability to adjust their behavior autonomously at run-time in response to their environment’s evolution. Samples of applications that require self-adaptation include Smart home systems and environmental monitoring. However, self-adaptivity is often constructed in an ad-hoc manner. Methods: In this paper, the authors present a pattern-based specification language for self-adaptive systems. Its semantics are presented in terms of fuzzy logic. Thus, enabling a meticulous processing of requirements, in order to permit the formulation of self-adaptive requirements accurately, thereby facilitates the design of systems that are flexible and responsive to adaptation in a systematic manner. Results: To show the applicability and effectiveness of our language, the authors apply it to two case studies. One case study reviews the Smart fridge in ambient assisted living and the second case study is focused on an ambulance dispatching system using a developed support tool.

Purpose: The aim of this article is to discuss the impact of static load balancing over a set of heterogeneous processors, where tasks are independent and unitary in static environments, by showing how to distribute task in order to optimize both the average response time and the degree of the resources used. Methods: Implementation of a modified scheduling algorithm, the latter is based on two parameters which are the execution time and the failure probability. The algorithm is based on the results of an optimal algorithm that already exists, with only one parameter that is execution time. Results: The obtained results show that the modified scheduling algorithm gives us the good results. Conclusion: The modified algorithm assumes that the processor has smallest execute time. So, the failure probability increases because of it's frequently use. The results obtained by testing this proposed algorithm are better than the optimal algorithm.