Broker-based service-oriented content adaptation framework

Electronic documents are becoming increasingly rich in content and varied in format and structure. At the same time, user preferences vary towards the contents and their devices are getting increasingly varied in capabilities. This mismatch between rich contents and user preferences along with the end device capability presents a challenge in providing ubiquitous access to these contents. Content adaptation is primarily used to bridge the mismatch by providing users with contents that is tailored to the given contexts e.g., device capability, preferences, or network bandwidth. Existing content adaptation systems employing these approaches such as client-side, server-side or proxy-side adaptation, operate in isolation, often encounter limited adaptation functionality, get overload if too many concurrent users and open to single point of failure, thus limiting the scope and scale of their services. To move beyond these shortcomings, this thesis establishes the basis for developing content adaptation solutions that are efficient and scalable. It presents a framework to enable content adaptation to be consumed as Web services provided by third-party service providers, which is termed as “service-oriented content adaptation”. Towards this perspective, this thesis addresses five key issues – how to enable content adaptation as services (serviceoriented framework); how to locate services in the network (service discovery protocol); how to select best possible services (path determination); how to provide quality assurance (service level agreement (SLA) framework); and how to negotiate quality of service (QoS negotiation). Specifically, we have: (i) identified the key research challenges for service-oriented content adaptation, along with a systematic understanding of the content adaptation research spectrum, captured in a taxonomy of content adaptation systems; (ii) developed an architectural framework that provides the basis for enabling content adaptation as Web services, providing the facilities to serve clients’ content adaptation requests through the client-side brokering; (iii) developed a service discovery protocol, by taking into account the searching space, searching time, match type of the services and physical location of the service providers; (iv) developed a mechanism to choose the best possible combination of services to serve a given content adaptation request, considering QoS levels offered; (v) developed an architectural framework that provides the basis for managing quality through the conceptualization of service level agreement; and (vi) introduced a strategy for QoS negotiation between multiple brokers and service providers, by taking into account the incoming requests and server utilization and, thus requiring the basis of determining serving priority and negotiating new QoS levels. The performance of the proposed solutions are compared with other competitive solutions and shown to be substantially better

AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach

تعتبر الكسافا محصولًا مهمًا في أجزاء كثيرة من العالم، لا سيما في إفريقيا وآسيا وأمريكا الجنوبية، حيث تعمل كغذاء أساسي لملايين الأشخاص. يعتبر استخدام ميزات اللون والملمس والشكل أقل كفاءة في تصنيف أنواع الكسافا. وذلك لأن أوراق الكسافا لها نفس لون مورفولوجيا بين نوع وآخر. بالإضافة إلى ذلك، فإن أوراق الكسافا لها شكل مشابه نسبيًا لنوع واحد من الكسافا، وبالمثل، مع قوام أوراق الكسافا. إلى جانب ذلك، هناك أيضًا المنيهوت السامة. الكسافا السامة وغير السامة لها لون وشكل وملمس أوراق متطابق نسبيًا. يهدف هذا البحث إلى تصنيف أنواع الكسافا باستخدام طريقة التعلم العميق مع AlexNet المدربة مسبقًا كمستخرج للميزات. تم استخدام ثلاث طبقات مختلفة متصلة بالكامل لاستخراج السمات، وهي fc6 و fc7 و fc8. كانت المصنفات المستخدمة هي Support Vector Machine (SVM) و K-Nearest Neighbours (KNN) و Naive Bayes. تتكون مجموعة البيانات من 1400 صورة لأوراق الكسافا تتكون من أربعة أنواع من الكسافا: Gajah و Manggu و Kapok و Beracun. أوضحت النتائج أن أفضل طبقة استخلاص كانت fc6 وبدقة 90.7٪ للطبقة المتناهية الصغر (SVM). كان أداء SVM أيضًا أفضل مقارنةً بـ KNN و Naive Bayes، بدقة 90.7٪، وحساسية 83.5٪، ونوعية 93.7٪، ودرجة F1 83.5٪. ستساهم نتائج هذا البحث في تطوير تقنيات تصنيف النباتات، وتوفير رؤى حول الاستخدام الأمثل للتعلم العميق وطرق التعلم الآلي لتحديد الأنواع النباتية. في النهاية، يمكن للنهج المقترح أن يساعد الباحثين والمزارعين وعلماء البيئة في تحديد الأنواع النباتية ومراقبة النظام البيئي والإدارة الزراعية.Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management

A Conceptual Study on Generic End Users Adoption of e-Government Services

This study proposes a conceptual model for examining factors affecting e-government adoption in developing countries. It includes evaluating the existing adoption model studies of e-government adoption. Preexisting theoretical model and comprehensive analysis of the various resources was chosen to guide this work, and the result revealed that additional external factors were also important for explaining the e-government adoption in developing countries. Hence, external factors such as trust, national culture, knowledge of e-government services and DeLone and McLean information system (D&M IS) success model also considered relevant were integrated with the unified theory of acceptance and use of technology (UTAUT) constructs as examining factors affecting e-government adoption. Finally, this study finds a formulation of the conceptual model and their relationship with all variables for explaining e-government adoption through a systematic justification of the proposed integrated model

Multi-criteria content adaptation service selection broker

In this paper, we propose a service-oriented content adaptation framework and an approach to the Content Adaptation Service Selection (CASS) problem. In particular, the problem is how to assign adaptation tasks (e.g., transcoding, video summarization, etc) together with respective content segments to appropriate adaptation services. Current systems tend to be mostly centralized suffering from single point failures. The proposed algorithm consists of a greedy and single objective assignment function that is constructed on top of an adaptation path tree. The performance of the proposed service selection framework is studied in terms of efficiency of service selection execution under various conditions. The results indicate that the proposed policy performs substantially better than the baseline approach.<br /

End-User Acceptance Of E-Government Services In an Indonesia Regency

The aim of this research to investigate citizen’s behaviour in e-Government adoption. The final purpose is tounderstanding of the public intention at the local governmentlevel, the of important things to make e-government servicebecome successful is public acceptance and willingness to use egovernmentservices. Therefore this study used a model ofUTAUT base on the unique problems which consists of six mainvariables that affect behavioral intention and use behavior, thesevariables are privacy, trust, performance expectancy, effortexpectancy, social influence, and facilitating condition. At thepractical level, the research aim to guide e-Government policydecision makers to better plan, design and implemet policies andstrategies

Extended TvX: A New Method Feature Based Semantic Similarity for Multiple Ontology

Semantic similarity between the terms is the main phase in information retrieval and information integration, which requires semantic content matching. Semantic similarity function is important in psychology, artificial intelligence and cognitive science. The problem of integrating various sources is the matching between ontological concepts. In this paper, we proposed to develop this method by analyzing the semantic similarity between the modeled taxonomical knowledge and features in different ontology. This paper contains a review on semantic similarity and multiple ontology that focuses on the feature-based approach. Besides that, we proposed a method, namely a semantic similarity that overcomes the limitation of different features of terms compared. As a result, we are able to develop a better method that improves the accuracy of the similarity measurement

Healthy food intake advisor using decision support system

: The difficulties to decide the food to eat and do not have enough knowledge that what foods should be avoided when pregnant or when facing some health problem. Healthy Food Advisor is an Android based application which acts as a healthy controller to all of the users. The purpose of developing this application is to suggest healthy food to users based on their personal condition in order to make them have a healthy lifestyle. Users are required to record all of the details such as age, height and weight, so the application and calculate the Body Mass Index (BMI) value and ca loric needs to user. Application will recommended the most suitable food lists to users according to their personal condition. Through this application, users no longer need to spend more time to think on a meal and busy to search from online that the nutrition information of food. The methodology used to develop this Android based application is Object-oriented Software Development (OOSD) model. Software technology used to develop this application is Ionic Framework where this technology uses web technology language to develop mobile hybrid application. Database used for this system is Firebase while programming language used to develop this application is AngularJS, HTML, TypeScript and SCSS. Hereby, this application is able to provide a simple and portable solution to help people decide the food and increase the knowledge of the public

Soft set theory based decision support system for mining electronic government dataset

Electronic government (e-gov) is applied to support performance and create more efficient and effective public services. Grouping data in soft-set theory can be considered as a decision-making technique for determining the maturity level of e-government use. So far, the uncertainty of the data obtained through the questionnaire has not been maximally used as an appropriate reference for the government in determining the direction of future e-gov development policy. This study presents the maximum attribute relative (MAR) based on soft set theory to classify attribute options. The results show that facilitation conditions (FC) are the highest variable in influencing people to use e-government, followed by performance expectancy (PE) and system quality (SQ). The results provide useful information for decision makers to make policies about their citizens and potentially provide recommendations on how to design and develop e-government systems in improving public services

A framework for formulating Malaysia’s public policy through citizen e-Participation

This paper addresses this key issue by enabling the participation of the citizen in formulating Malaysia’s public policy through e-participation.Currently, there is no e-participation framework for citizen to involve in Malaysia’s public policy formulation. Most public policy formulate by certain group of expertise but the feedback from citizen still lacking. The research embraces the socio-technical research paradigm and uses an Actor-Network Theory (ANT) as the theoretical foundation to explore the mutual interaction between all the actors.The proposed e-participation framework has been validated using Delphi Method and evaluated by experts is shown to be workable and practical