Jabar H. Yousif

Enhanced inquiry method for malicious object identification

This paper proposes a new technique for malicious object detec-tion and identification. The technique is based on a concept of vi-rus inquiry. The inquiry is an activity that is performed by the malicious object during its initiation. The malicious object uses this activity to ensure its uniqueness in memory. The inquiry can be regarded as a common behavior of malicious object such as viruses. The proposed system is designed using the concept of Ob-ject Oriented Programming (OOP) that treats the operating system, user program, and virus as objects. It is constructed of three ele-mentary objects that perform their activities depending on two da-tabases.

Cloud Computing and Accident Handling Systems

An attempt is made to study the current issues of the cloud computing solutions for the life critical systemcar accident systems in the Gulf region. Gulf region has high death rate because of car accidents and there is little or no proper accident handling facilities in the region. This Research paper includes the review of development in the field of cloud computing in the service industries which provide some assistance to the accident handling systems including the hospitals and the drivers using latest technologies such as Mobile computing, SaaS, Cloud Computing etc..

Forecasting Hydrogen Sulfide Level Based Neural Computation

This paper aims to design and implement an environmental monitoring and forecasting system based on neural computing approach. The output information is used for feeding the alarming systems. The data are collected in real-time through pollution monitoring sensors at Sohar region. Air pollution is a serious problem and coming from different sources that can lead to a catastrophic, which is needed to be monitored and controlled.The proposed work is monitored and managed the alerts on the emissions of Hydrogen sulfide (H2S) in Oman. It is forecasting the Level of H2S in the Sohar region based Neural Computation. The SOFM is used to compute and predict the ratio of H2S and then issue an alarm to take the proper decision which helps to implement the necessary precautions. The experiments are giving evidence that the predicted values are closely to true values that gained from real sensors with accuracy of 78% and less MSE of 0.03865.

Environmental Scrutinizing System based on Soft Computing Technique

Artificial intelligent techniques are very much needed to design the environmental monitoring systems. These systems must be smart enough so that all the decisions taken by the system must be accurate. Soft Computing (SC) it is a set of computational methods that attempt to determine satisfactory approximate solutions to find a model for real-world problems. It based on various techniques such as Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms. The aim of this paper is to implement a soft computing technique which is artificial neural network based on Self-Organizing Feature Map (SOFM). SOFM model for monitoring and collecting of the data are real-time and static datasets acquired through pollution monitoring sensors and stations. In the environmental monitoring systems the ultimate requirement is to establish controls for the sensor based data acquisition systems and need interactive and dynamic reporting services. SOFM techniques are used for data analysis and processing. The processed data is used for control system which even feeds to the alarming systems.

Natural Language Processing based Soft Computing Techniques

paper presents the implementation of soft computing (SC) techniques in the field of natural language processing. An attempt is made to design and implement an automatic tagger that extract a free text and then tag it. The part of speech taggers (POS) is the process of categorization words based on their meaning, functions and types (noun, verb, adjective, etc). Two stages tagging system based MPL, FRNN and SVM are implemented and designed. The system helps to classify words and assign the correct POS for each of them. The taggers are tested using two different languages (Arabic and Hindi). The Word disambiguation issue has been solved successfully for Arabic text. Experience has shown that the proposed taggers achieved a great accuracy (99%).

Car Accident Notification Based on Mobile Cloud Computing

The aims of this paper is to design and implement a mobile application for car accident notification. It sends an electronic report and notification including the location and type of incidents. Road traffic accidents (RTA) are one of the main causes of death across the Gulf Cooperation Council (GCC). Governments are spending Billions of dollars in the building road and infrastructure in order to reduce the number of RTA fatalities. Cloud Computing can play a significant role in reducing the number of RTA fatalities. In order to avoid many cases of deaths, a post-accident emergency reporting system is much needed. Gulf Arab countries are recorded the highest number of deaths and injuries because of road accidents. Therefore, the proposed notification application apps help to notify an accident immediately using easy steps, which will save the life of many people.

Soft Computing Techniques for Mishaps Prediction

An attempt is made to implement the soft computing techniques in the prediction of the mishaps behaviors. The main objective of this paper is to implement the Multilayer Perceptron (MLP) neural network topology in mishap analysis and prediction. Efforts are made to summarize past research on the road mishaps causes and their analysis techniques and point out their weaknesses. Indicate new ideas in this area and Identify research directions leading to successful development and use of analytical techniques in the area of mishap analysis leading to Road safety General Terms Mishaps, Computer Science, Safety System, Soft Computing, ITS Systems,