IT

Today managing files in a server system has the same magnitude as managing the World Wide Web due to the dynamic nature of the file system. Even searching for files over the file system is time consuming because finding a file on hard disk is a long-running task. Every file on the disk has to be read with dangling pointers to files which no longer exist because they have been changed, moved or deleted. This makes the user frustrated. The Automatic file indexing framework facilitates users to resolve file names and locate documents stored in file repositories. The main design objective of the framework is to maintain sub-indexes at the folder level that have the full knowledge of the revisions that are made at the folder level automatically. This research proposes a framework that manages the creation and maintenance of the file index, with the use of Resources Description Framework (RDF) and retrieval using semantic query languages i.e. SPARQL. The sub-indexes are maintained hierarchically starting from the leaf node to the root node recursively. The proposed framework will monitor the file system continuously and update individual folder descriptors (sub-indexes) stored on each node as the file system changes making the cached indexes resilient to any file changes. The framework is resilient of file or folder name changes. Further, the study explores avenues to build an offline semantic index that can be used by the clients to perform distribute file search without performing the search on the server itself. This is viable since the framework uses semantic languages to describe and build file descriptors that can easily integrate semantic indexing and hence this makes the index readily available for the Web.

In which we see how an agent can find a sequence of actions that achieves its goals, when no single action will do. The simplest agents discussed in Chapter 2 were the reflex agents, which base their actions on a direct mapping from states to actions. Such agents cannot operate well in environments for which this mapping would be too large to store and would take too long to learn. Goal-based agents, on the other hand, can succeed by considering future actions and the desirability of their outcomes. This chapter describes one kind of goal-based agent called a problem-solving agent.

Nowadays, humans' health is deteriorating by dermatological diseases, and the spreading rate is high. Most people are not aware of skin diseases. As they do not realize these diseases' seriousness, they try to treat with some remedies by themselves, even without knowing what the actual disease is. Nevertheless, it is not a suitable way to cure the disease, leading to future complications. So still the dermatological diseases remain as one of the main categories of common health issues. A few people prefer to use computerized systems to evaluate the disease conditions these days. Moreover, it is essential to know about the diseases to manage that condition and prevent escalation. Therefore, the proposed system is implemented to give users some knowledge about dermatological diseases as much as possible. The users can get awareness and predict skin diseases and complications from the data mining technique. The user can identify the stage of the dermatological disease by applying the classification algorithm. Furthermore, this system will also scrap web pages related to that disease from known or system verified websites. The content analysis is based on the machine learning process, especially using Neural Language Processing. Hence, the system will undeniably be useful to the users to summarize skin diseases and get concerns from a dermatologist.

Regardless of the mobile applications available in smartphones, the veterinary hospitals in Sri Lanka still use the manual books to fill the vaccination schedule and keep track of pet care. This research is an attempt to change this tradition by introducing a smart system for dogs. The system can be used both by dog lovers and veterinary doctors. In this study, new proposed application plan is to replace the traditional method with a method that can store the information into a system and make it available for the users to take care of the dog. Since the dog's birth, the information can be stored and it will assist the pet owners. Unlike other applications available, the advantage of this application is that the user can choose applications to be used individually or integrated according to their choice.

E-learning is a form of providing education by using electronic devices. Lack of proper mechanisms for encouraging and assisting students are key issues faced by many students in an e-learning environment. The 'Vidu Mithuru' is a question-based e-learning application which has been developed as a solution to overcome these problems. This mobile application will auto generate and categorize the questions, evaluate the answers and track the performance while providing motivational quotes by detecting the emotions of the student. This mobile application is based on Neural Networks, Natural Language Processing and Machine Learning concepts. In order to developing this application, the information provided by the primary education professionals was used to comply with the standards. The core objective of the proposed solution is to track the performance level and assist the students to improve in their studies while keeping them motivated. The trained Machine Learning models have achieved the accuracy of 75%, 78%, 99% and 86% for question categorization model, speech emotion detection model, facial emotion detection model and model to evaluate answers as respectively. We have received favorable responses as the results after testing the developed 'Vidu Mithuru' mobile application among students in 3 rd , 4 th and 5 th grades in school.

Motorcycles are an important part of daily transportation. They are specially used to avoid traffic congestions downtown and most of the individuals tend to use a motorcycle because it takes less time to get to the destination. The rapid increase of motorcycle usage has led to a significant increase in the number of motorcycle-related accidents and fatalities. The reasons for accidents which were considered are speeding, collision with objects, lack of focus or drowsiness where only the rider's head is protected but not the body. And accidents may lead to death when help is not called immediately. By considering these, author introduces a Motorcyclists Safety Assistant Application (MSAA). This research tries to address four major factors that caused most road accidents and fatalities in Sri Lanka. They are excess speed, 360-degree threat detection, motorcyclist safety balloons, and emergency alert system. Here, MSAA can detect the vehicle's real-time speed and inform the user when a certain speed limit has been exceeded. Also, it has proposed a system which automatically detects threats that occur in each collision and alerts the rider via visual and audio cues. Moreover, the next system will be focusing on safeguarding the rider's body by inflating an airbag which will be connected to the rider's jacket. An automatic alert system is also introduced where the main objective is to mitigate the consequences of accidents by sending a message to the registered mobile using wireless communication techniques and checks whether an accident has occurred using vibration frequency limits. Location will be sent through the tracking system to cover the geographical coordinates over the area. The proposed domain successfully contributes to a drastic reduction in road accidents. The ultimate objective is to create a better future for everybody through road safety. The survey conducted to test the user satisfactory level, demonstrated high user satisfaction.

Welcome to the 2021 edition of Australian Jobs. This publication
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