@article {847, title = {A Brief Review of the Automation of Dependency Satisfaction With in Microservices Architectures}, journal = {Journal of Digital information Management}, volume = {22}, year = {2024}, month = {09/2024}, pages = {99-107}, type = {Research}, chapter = {99}, abstract = {

Accelerated advances in network speed, re- liability, and security have increased the demand for soft- ware and services to move from being stored and pro- cessed locally on users{\textquoteright} devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software archi- tectures and software architectures that meet these new requirements. One example of software architecture is the recent emergence of a microservices architecture to meet online service providers{\textquoteright} maintenance and scalability requirements. Microservices is an architecture style that focuses on breaking down a system into small, lightweight services that are intentionally created to perform a highly cohesive business function, and it is an evolution of the traditional service-oriented architecture style. The practi- cal projection of microservices architecture in informa- tion engineering is several small applications that work together to provide an integrated service that appears to the external medium as a single application. Each of these applications has its own independent source code and deals with data sources that are different from the rest. As a result, it has an independent development team. Partial applications depend on each other to accomplish their functions, and therefore, any modification to one of the services will affect the work of the rest of the ser- vices; in large applications that require keeping the sys- tem always available, we must perform the process of publishing updates for parts of the system without stop- ping it completely, so we must monitor the impact of modi- fications and forecast system status before new updates are deployed. In achieving this prediction, we must know the dependencies between different services to arrive at a cognitive model representing the dependencies between services or applications.

}, doi = {http://www.dline.info/jdim/wp-content/uploads/3.pdf}, url = {https://10.0.23.137/jdim/2024/22/3/99-107}, author = {Ammar Esrawi and Bassel Alkhatib} } @article {846, title = {Can ChatGPT Predict Stock Market Price Movements?}, journal = {Journal of Digital Information Management}, volume = {22}, year = {2024}, month = {09/2024}, pages = {91-98}, type = {Research}, chapter = {91}, abstract = {

Accelerated advances in network speed, re- liability, and security have increased the demand for soft- ware and services to move from being stored and pro- cessed locally on users{\textquoteright} devices to being managed by third parties that can be accessed through the network. This has led to the need to develop new software archi- tectures and software architectures that meet these new requirements. One example of software architecture is the recent emergence of a microservices architecture to meet online service providers{\textquoteright} maintenance and scalability requirements. Microservices is an architecture style that focuses on breaking down a system into small, lightweight services that are intentionally created to perform a highly cohesive business function, and it is an evolution of the traditional service-oriented architecture style. The practi- cal projection of microservices architecture in informa- tion engineering is several small applications that work together to provide an integrated service that appears to the external medium as a single application. Each of these applications has its own independent source code and deals with data sources that are different from the rest. As a result, it has an independent development team. Partial applications depend on each other to accomplish their functions, and therefore, any modification to one of the services will affect the work of the rest of the ser- vices; in large applications that require keeping the sys- tem always available, we must perform the process of publishing updates for parts of the system without stop- ping it completely, so we must monitor the impact of modi- fications and forecast system status before new updates are deployed. In achieving this prediction, we must know the dependencies between different services to arrive at a cognitive model representing the dependencies between services or applications.

}, doi = {https://10.0.23.137/jdim/2024/22/3/91-98}, url = {http://www.dline.info/jdim/wp-content/uploads/2.pdf}, author = {Ammar Esrawi and Bassel Alkhatib} } @article {749, title = {ASR Features Extraction Using MFCC And LPC: A Comparative Study-}, journal = {Journal of Digital Information Management}, volume = {21}, year = {2023}, month = {06/2023}, pages = {39-49}, type = {Research}, chapter = {39}, abstract = {

The field of Automatic Speaker Recognition (ASR) is an important and open field for researchers and scientists, especially as it has become essential in facilitating the work we do in our daily lives. Such as digital authentication and electronic transactions, and consider It as a secure environment to authenticate users{\textquoteright} access to their accounts. Many technologies have been developed in the field of recognition but so far, there is no complete tool or method for speaker identification, the most important step in ASR is the extraction of voice features. Many methods and tools can be used to extract the speaker{\textquoteright}s vocal characteristics (voice features), which in turn will identify the user and recognize his voice spectrum through the phonetic linguistic message. In this paper, two methods will be studied, each using a different technique MFCC, which uses a logarithmic scale, and LPC, which uses a linear scale. The method used in ASR should have minimal error because it is an important authentication technology like a fingerprint, where two different people cannot have the same voice spectral range (voiceprint).

}, doi = {https://doi.org/10.6025/jdim/2023/21/2/39-49}, url = {http://www.dline.info/download.php?sn=3757}, author = {Bassel Alkhatib and Mohammad Madian Kamal Eddin} } @article {748, title = {A Step towards Semantic Digital Library in the Arabic Region-}, journal = {Journal of Digital Information Management}, volume = {21}, year = {2023}, month = {06/2023}, pages = {29-38}, chapter = {29}, abstract = {

The use of semantic web technologies is very limited in Arab libraries, even with the existence of important historical and literary documents, and the great digitization movement now, the documents are still trapped within the databases of the separated libraries and are not visible to search engines. The research aims to Study the applications of semantic web technologies in catalogs of digital, hybrid libraries and catalogs of public libraries, exploring the main ontologies and conceptual Data models used in the field, and the possibilities of reusing them with Arab libraries, or the need to build new ontologies, studying the necessary steps, tools, and best practices, define the difficulties and challenges facing the Arabic semantic library and searching for possible technical solutions, our experiments target the heterogeneous databases of small, medium or large libraries, considering that some Arabic library catalogs are not following a standard Data Model, to contribute to the research and development of integrating bibliographic data of Arab libraries and moving towards the Semantic web and Linked Open Data.

}, doi = {https://doi.org/10.6025/jdim/2023/21/2/29-38}, url = {http://www.dline.info/download.php?sn=3756}, author = {Maysah Alsahar and Bassel Alkhatib} } @article {757, title = {ASR Features Extraction Using MFCC and LPC: A Comparative Study-}, journal = {Journal of Digital Information Management}, volume = {20}, year = {2022}, month = {09-2022}, pages = {79-89}, type = {Research}, chapter = {79}, abstract = {

The field of Automatic Speaker Recognition (ASR) is an important and open field for researchers and scientists, especially as it has become essential in facilitating the work we do in our daily lives. Many technologies have been developed in the recognition field. Such as digital authentication and electronic transactions; consider It a secure environment to authenticate user{\textquoteright}s access to their accounts. Still, so far, there is no complete tool or method for speaker identification, the most crucial step in ASR is the extraction of voice features. Many techniques and tools can extract the speaker{\textquoteright}s vocal characteristics (voice features), identifying the user and recognising his voice spectrum through the phonetic and linguistic message. In this paper, two methods will be studied, each using a different technique MFCC, which uses a logarithmic scale, and LPC, which uses a linear scale. The method used in ASR should have a minimal error because it is a crucial authentication technology like a fingerprint, where two different people cannot have the same voice spectral range (voiceprint).

}, doi = {https://doi.org/10.6025/jdim/2022/20/3/79-89}, url = {http://www.dline.info/download.php?sn=3566}, author = {Mohammad Madian Kamal Eddin and Bassel Alkhatib} } @article {755, title = {Deep Learning Model CNN With LSTM For Speaker Recognition}, journal = {Journal of Digital Information Management}, volume = {20}, year = {2022}, month = {12/2022}, pages = {131-147}, type = {Research}, chapter = {131}, abstract = {

Speech recognition is one of the most important research fields nowadays because of its necessity in our daily lives and to raise the fields of security to the highest level, It{\^a}{\texteuro}{\texttrademark}s a task of speech processing, and our main scope in this paper is on speaker verification, which is to identify persons from their voices where the process depends on digitizing the sound waves into a form that allows the system to deal with it. The verification process is based on the characteristics of the speaker{\textquoteright}s voice (voice biometrics) and sends it to a further process to extract the features of that voice using the feature extraction method and using AI techniques to perform the task of identification. MFCC is used for the task of features extraction and obtains the spectrogram of a given voice signal where it represents a bank of information about the voice and sends it to the CNN model for further processing for training the model on that signal to verify if the voice belongs to a user in the system or it{\^a}{\texteuro}{\texttrademark}s a new enrollment.

}, author = {Bassel Alkhatib and Mohammad Madian and Kamal Eddin} } @article {761, title = {A Survey of Sentiment Analysis in the Arabic Language}, journal = {Journal of Digital Information Management}, volume = {20}, year = {2022}, month = {03/2022}, pages = {10-24}, type = {Research}, chapter = {10}, abstract = {

\ {\^a}{\texteuro}{\oe}What the others think?{\^a}{\texteuro} is an essential question for individuals, companies, and governments. All need to know the public opinions to make their decisions wisely. In the last decade, sentiment analysis and opinion mining have become one of the growing research areas. This paper presents the current state of sentiment analysis and opinion mining research. In particular, researches those deals with the Arabic language. We tried to cover the techniques and methods in sentiment analysis and the challenges in the field. We described the leading methods and approaches that have been introduced in the literature for Arabic Sentiment analysis and opinion mining. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles about Arabic Sentiment analysis. These articles are categorized according to their contributions in the various sentiment analysis techniques.

}, doi = {https://doi.org/10.6025/jdim/2022/20/1/10-24}, url = {http://www.dline.info/download.php?sn=3449}, author = {Kamel JAFAR and Bassel Alkhatib and Hazem WANNOUS} } @article {710, title = {Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {04/2019}, pages = {51-60}, type = {Research}, chapter = {51}, doi = {https://doi.org/10.6025/jdim/2019/17/2/51-60}, url = {http://dline.info/fpaper/jdim/v17i2/jdimv17i2_1.pdf}, author = {Bassel Alkhatib and Randa Basheer} } @article {805, title = {Crawling the Dark Web: A Conceptual Perspective, Challenges and Implementation{\textendash}}, journal = {Journal of Digital Information Management}, volume = {17}, year = {2019}, month = {04/2019}, pages = {51-60}, type = {Research}, chapter = {51}, abstract = {

\ Internet and network technologies have evolved dramatically in the last two decades, with rising users{\textquoteright} demands to preserve their identities and privacy. Researchers have developed approaches to achieve users{\textquoteright} demands, where the biggest part of the internet has formed, the Deep Web. However, as the Deep Web provides the resort for many benign users who desire to preserve their privacy, it also became the perfect floor for hosting illicit activities, which generated the Dark Web. This leads to the necessity of finding automated solutions to support law and security agencies in collecting information from the Dark Web to disclose such activities. In this paper, we illustrate the concepts needed for the development of a crawler that collects information from a dark website. We start from discussing the three layers of the Internet, the characteristics of the hidden and private networks, and the technical features of Tor network. We also addressed the challenges facing the dark web crawler. Finally, we presented our experimental system that fetches data from a dark market. This approach helps in putting a single dark website under investigation, and can be a seed for future research and development.

}, doi = {https://doi.org/10.6025/jdim/2019/17/2/51-60}, url = {http://dline.info/fpaper/jdim/v17i2/jdimv17i2_1.pdf}, author = {Bassel Alkhatib and Randa Basheer} } @article {676, title = {Building a Herbal Medicine Ontology Aligned with Symptoms and Diseases Ontologies}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {06/2018}, pages = {114-126}, type = {Research}, author = {Bassel Alkhatib and Dania Briman} } @article {826, title = {Building a Herbal Medicine Ontology Aligned with Symptoms and Diseases Ontologies {\textendash}}, journal = {Journal of Digital Information Management}, volume = {16}, year = {2018}, month = {06/2018}, pages = {114-126}, type = {Research}, chapter = {114}, abstract = {

The use of traditional medicine is increasing rapidly in many societies due to its efficiency and affordability. The locally available herbs are basically used in the treatment of symptoms and diseases. Herbal Medicines have proved effective in many cases and outperformed the chemical drugs.

}, doi = {https://doi.org/10.6025/jdim/2018/16/3/114-126}, url = {http://dline.info/fpaper/jdim/v16i3/jdimv16i3_2.pdf}, author = {Bassel Alkhatib and Dania Briman} }