International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
Not a member yet
459 research outputs found
Sort by
An Efficient Feature Selection Algorithm for Health Care Data Processing
Get PDFThe researcher used to study the tides depends on a qualitative approach that takes into account the review of past works and studies of various authors and researchers. The service sector is an explosive part of the economy in many countries. Its development is fraught with difficulties, including increased costs, wasteful aspects, poor quality, and the expansion of multifaceted nature. AI systems can be deployed in health programs they want to be qualified using statistics obtained from clinical activities, consisting of screening, diagnosis, corrective measures, etc. The advantage is due to proactive behavior and specialized medical services. Stimulates e-health and electronic monitoring at the forefront of research. AI systems can be deployed in health programs they want to be “qualified” using statistics obtained from clinical activities, consisting of screening, diagnosis, corrective measures, etc. On the other hand, among the various classes in a study in medical services, the use of data mining is usually used as an aid in clinical choice (42%) and for managerial purposes (32%). This segment examines the use of data mining in these territories, and the main points of these checks, performance holes, and key points are different
Observational Discoveries in Agile Methodologies and Extreme Programming
Get PDF The In this study we have focused on various methodologies of nimble programming advancement, for example, Extreme Programming, Crystal Clear, Scrum, Lean programming improvement and some others methods related to category. As there are several methods related to agile development, but we have mainly focused on some of the important methodologies, discovered so far []. This study also reveals the criticism over some of the agile methodologies, based on some of its parameters, while in some situations favor is given to the traditional methodologies. We have adopted quantitative and qualitative approaches to carry out this work, the major audience involved were professionals, software developers who were working in the industry, and were the real practitioners of these methodologies, by taking advantage of their experiences we have considered their suggestions, ideas and experiences. Any software development project involves certain parameters: productivity, quality, cost and schedule. These project parameter are at the main theme of our study, based on it we have discovered that how agile methods may influence the software development industry
Learning and adaptation strategies for evolving artifact capabilities
Get PDFIn this study we address enhancing the ability of social agents embedded in multi-agent based simulations to achieve their goals by using objects in their environment as artifacts. Reformulated as a discrete optimization problem solved with evolutionary computation methods, social agents are empowered to learn and adapt through observations of their own behavior, others in the environment and their community at large. An implemented case study is provided incorporating the model into the multi-agent simulation of the Village EcoDynamics Project developed to study the early Pueblo Indian settlers from A.D. 600 to 1300. Eliminating the existing presumption that agents automatically know the productivity of the landscape as part of their settling and farming practices, agents use the landscape as an artifact, learning to predict its productivity from a few attributes such as the area\u27s slope and aspect. Given the dynamic nature of the landscape and its inhabitants, agents also evolve various combinations of learning strategies adapting to meet their needs. The result is the demonstration of a mechanism for incorporating artifact use learning and evolution in social simulations, leading to the emergence of favorable learning strategies
Forensics Based SDN in Data Centers
Get PDFRecently, most data centers have adopted for Software-Defined Network (SDN) architecture to meet the demands for scalability and cost-efficient computer networks. SDN controller separates the data plane and control plane and implements instructions instead of protocols, which improves the Quality of Services (QoS) , enhances energy efficiency and protection mechanisms . However, such centralizations present an opportunity for attackers to utilize the controller of the network and master the entire network devices, which makes it vulnerable. Recent studies efforts have attempted to address the security issue with minimal consideration to the forensics aspects. Based on this, the research will focus on the forensic issue on the SDN network of data center environments. There are diverse approaches to accurately identify the various possible threats to protect the network. For this reason, deep learning approach will used to detect DDoS attacks, which is regarded as the most proper approach for detection of threat. Therefore, the proposed network consists of mobile nodes, head controller, detection engine, domain controller, source controller, Gateway and cloud center. The first stage of the attack is analyzed as serious, where the process includes recording the traffic as criminal evidence to track the criminal, add the IP source of the packet to blacklist and block all packets from this source and eliminate all packets. The second stage not-serious, which includes blocking all packets from the source node for this session, or the non-malicious packets are transmitted using the proposed protocol. This study is evaluated in OMNET ++ environment as a simulation and showed successful results than the existing approaches
Exploring Impacts of Random Proactive Jamming Attack in Wireless Sensor Network
Get PDFWireless Sensor networks are one of the most extensively used technologies in our day to day lives; they can provide communication without needing a fixed infrastructure, which makes them suitable for communication in disaster areas or when quick deployment is needed. However, this kinds of network technology uses the wireless medium for communication. It is vulnerable to malicious attacks. One of the most frequently used attacks is a random jamming attacks which is Denial of Service attack. Random Jamming attacks disturb the communication between Sink and legitimate nodes. In rough environments where there is constant traffic, Random jamming attack can cause serious problems. Because of this, a study of random jamming attacks and how to prevent them is necessary. In this research the random jamming attacks were simulated using Riverbed Modeler software, in order to provide a better understanding of effects of random jamming attacks. This study will be helpful for future research and development of a practical, effective way to avoid random jamming attacks. The objectives of this thesis were to simulate and analysis wireless sensor network (ZigBee) under random jamming attacks; launch different kinds of (fixed, mobile) random jamming attacks in order to test how much influence on performance of wireless sensor network. Riverbed Modeler Based simulation which have five scenarios were created and the simulation was run and the results were collected, which shows that the throughput of the wireless sensor network decrease and increase the delay ,data drop when the network is affected by the random Jammers. Finally, thesis describe the open issues in this field, such as adding more than one random jammer and sink in wireless sensor network
Usages of Semantic Web Services Technologies in IoT Ecosystems and its Impact in Services Delivery: A survey
Get PDFInternet of things (IoT) has begun to emerge in our daily life through the huge number of smart services provided by the devices that deploy around us. Vague and uncertainty in attributes that using in describing services, different levels of quality of each service and the limitation in capabilities of IoT devices are affect and hinder the process of discovering or selecting services. The services in IoT need to be well described to enable users to receive their services that relevant to their query. This survey will investigate the most popular semantic services models and explore the use of these models in enhancing services discovery and services selection in IoT domain. Furthermore, the survey will investigate the evaluation metrics used by each study and compare the results that they obtained. 
Human Face Detection: Manual vs. Kohonen Self Organizing Map
Get PDFIn today\u27s world it is very much important to maintain the security of information and its risks. The biometric-based techniques are very much useful in these problems. Among the several kinds of biometric-based technique, face detection is much complex and much more important. Due to the age and several other problems, a human face structure changes over time, again a human has lots of expressions. Sometimes due to the lighting condition or the variation of the angle of an input device, the pattern of a human face structure also changed. As a result, the face cannot be detected properly. In this paper, a method is proposed that can detect the human faces both automatically and manually very efficiently. In manual mode, a user can select the input faces referred by the system according to their choice. In automated mode, the system detected all possible face areas using the Kohonen Self-Organizing Feature Map technique. This method reduced the complex color image into a vector quantized image with desired colors. Then a color segmentation technique is used to detect the possible face skin areas from the vector quantized image. Then the Histogram Oriented Gradient technique used to detect the feature from the faces and K-Nearest Neighbor Classifier is used to compare both face images detected by the two modes. The automated method prosed better accuracy than the manual method
Smart Touch Attendance Management System Using NFC Tag: Improving Learning Outcomes
Get PDFGiven the need to maximize information technology to optimize attendance management systems, based on its apparent shortcomings, the aim of the study is to develop, test, appraise and analyze a NFC tag automated attendance system in an educational learning environment to improve learning outcomes. We used unified modeling language, PHP and Aptana Studio to develop a system based on the Radio Frequency Identification (RFID) technology. It operates at 13.56 MHz and relies on ISO14443 and ISO 18092 for low level data exchange between two NFC devices to automate attendance, which can be used by educational institutions and other organizations for efficient, effective and sustainable attendance database management. It was discovered that variables such as time, cost, energy inherent in the use of traditional systems were not significant factors as they were optimized to add value to the organization. This work adds value to the management of organizations’ processes, people and product affording them opportunity to maximize time, cost, effort and energy
Automatic Plant Detection Using HOG and LBP Features With SVM
Get PDFPlants play a vital role in the cycle of nature. Plants are the only organisms which produce food by converting light energy from the sun. They also help in maintaining oxygen balance on earth by emitting oxygen and taking carbon dioxide. They have plenty of use in medicine and industry. But plant species are vast in number. To identify this large number of existing plant species in the world is a tedious and time-consuming task for a human. Hence, an automatic plant identification tool is very useful even for experienced botanists to identify the vast number of plants. In this paper, we proposed a technique to identify the plant leaf images. For training and testing, we used a publicly available dataset called Flavia leaf dataset. Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) are used to extract features and multiclass Support Vector Machine (SVM) is applied to classify the leaf images. We observed that the accuracy of HOG+SVM with HOG feature extraction using cells size of 2 x 2, 4 x 4 and 8 x 8 are 77.5%, 81.25% and 85.31 respectively. The accuracy of LBP+ SVM is 40.6% and the combination of HOG and LBP based features with SVM achieved 91.25% accuracy. The experimental results indicate the effectiveness of HOG+LBP with SVM over HOG+SVM and LBP+SVM techniques.
Upgraded Deadlock Averting Algorithms in Distributed Systems
Get PDFDistributed system deadlock is like ordinary deadlock but it is difficult to prevent or detect when it is traced down. In the distributed system all, the related information is distributed over many machines. However, deadlock in distributed systems is tremendously serious. Therefore, it is important to understand how this deadlock is different from the ordinary deadlock and how to prevent it. To prevent deadlock in the distributed system there are two techniques to prevent it one wound-wait and other is wait-die. Therefore, the problem in these algorithms are that they just attend to the timestamp of the process but not the priority of them but in the real operating system priority of the process is very important. In this paper, we present upgraded deadlock averting algorithms and these algorithms are deal with both priority and time stamp of processes