Designing a library management system for Gazi Husrev-beg library using data structure and algorithm

Islamic manuscripts, documents, and religious books go way back in the early ages. These have been preserved at Balkans in the library named Gazi Husrev-beg, Library in an extensive manner. The premises are situated in the vicinity of a mosques complex called Sarajevo where the massive collection is articulated and catalogued in a brand new manner. It has a rich database in learning the Ottoman Empire and more specifically the Balkans in precision. The library was officially started in 2014 stating to be a state-of-art for the aspirers, researchers, historians, archeologists and every enthusiast as well as for general public. This research paper is devised in order to formulate a structure for library management system that can help preserve the age old assets like books, manuscripts, journals, articles, DVDs, etc. Everything is electronically managed and stored in a peculiar format. In order to keep a note on the entire system, the structure is beneficial to students, professors, librarians, public visitors etc. The process of issuing a given entity (book, or journal, or manuscript or anything from the library) and returning is kept a digital track and predefined penalty is cited to the individual if the returning of entity is late, or damaged, or misplaced. To maintain this procedure in a manual format might be confusing and tedious. To overcome the stress of maintaining this manually, a data structure is prepared, and algorithms are formed to get access of the library from multiple places of Gazi Husrev-beg Library

Designing and configuring context-aware semantic web applications

Context-aware services are attracting attention of world as the use of web services are rapidly growing. We designed an architecture of context-aware semantic web which provides on demand flexibility and scalability in extracting and mining the research papers from well-known digital libraries i.e. ACM, IEEE and SpringerLink. This paper proposes a context-aware administrations system, which supports programmed revelation and incorporation of setting dependent on Semantic Web administrations. This work has been done using the python programming language with a dedicated library for the semantic web analysis named as “Cubic-Web” on any defined dataset, in our case as we have used a dataset for extracting and studying several publications to measure the impact of context aware semantic web application on the results. We have found the average recall and averge accuracy for all the context aware research journals in our research work. Moreover, as this study is limited journal documents, other future studies can be approached by examining different types of publications using this advance research. An efficient system has been designed considering the parameters of research article meta-data to find out the papers from the web using semantic web technology. Parameters like year of publication, type of publication, number of contributors, evaluation methods and analysis method used in publication. All this data has been extracted using the designed context-aware semantic web technology

Survey: Benefits of integrating both wireless sensors networks and cloud computing infrastructure

Cloud computing has the capabilities of powerful processing and scalable storage with the ability of offline and online data analysis and mining of the collected sensed data from body areas networks. Cloud computing can be considered as the main enabler for modern manufacturing industries. Cloud computing can efficiently serve key areas of manufacturing by aspects of the pay-as-you-go business model, scaling up and down production according to certain demands, more customized solutions, and flexible deployments. In cloud manufacturing, the distributed sensors and resources can be managed in centralized architecture that allows cloud users to request more specific product design, testing at all the stages of the product. This study covers the main points of Integrating Both Wireless Sensors Networks and Cloud Computing Infrastructure and gives a view of the various advantage and disadvantages of methods in integration

A survey: Issues and challenges of communication technologies in WBAN

Wireless Body Area Network (WBAN) refers to a group of small intelligent electronic devices placed on the human body to monitor its vital signals. It provides a continuous health monitoring of a patient without any constraint on his/her normal daily life activities through the health care applications. Due to the strong heterogeneous nature of the applications, data rates will vary strongly, ranging from simple data at a few Kbits/s to the video stream of several Kbits/s. Data can also be sent in bursts, which means that it is sent at a higher data rate during the bursts. This study covers the main requirements of communication technologies that are used in WBAN comprise of two major parts. The first part, which presents the short-range classification, gives a specialized outline of a few standard wireless technologies that are short-ranged. These are introduced as contenders for intra-BAN communications for communications inside a Body Area Network (BAN) and between the elements

Innovative technologies of wireless sensor network: The applications of WBAN system and environment

Wireless Body Area Networks (WBAN) have risen as a key innovation for providing real-time patient health care and diagnosing many life-threatening diseases. The principle adopted by health service providers is providing convenience for patients in need of health care, allowing them comfortable with the movement and constant monitoring with a reduction in health care costs. This study presented the basic concept and general background of the main WBAN system with applications. The current models for on-body and embedded WBANs, sensor node features, and sensor schemes for information via negation are used in the WBAN

Implement DNN technology by using wireless sensor network system based on IOT applications

The smart Internet of Things-based system suggested in this research intends to increase network and application accuracy by controlling and monitoring the network. This is a deep learning network. The invisible layer's structure permits it to learn more. Improved quality of service supplied by each sensor node thanks to element-modified deep learning and network buffer capacity management. A customized deep learning technique can be used to train a system that can focus better on tasks. The researchers were able to implement wireless sensor calculations with 98.68 percent precision and the fastest execution time. With a sensor-based system and a short execution time, this article detects and classifies the proxy with 99.21 percent accuracy. However, we were able to accurately detect and classify intrusions and real-time proxy types in this study, which is a significant improvement over previous research

Enhancing quality of service in IoT through deep learning techniques

When evaluating an Internet of Things (IoT) platform, it is crucial to consider the quality of service (QoS) as a key criterion. With critical devices relying on IoT technology for both personal and business use, ensuring its security is paramount. However, the vast amount of data generated by IoT devices makes it challenging to manage QoS using conventional techniques, particularly when attempting to extract valuable characteristics from the data. To address this issue, we propose a dynamic-progressive deep reinforcement learning (DPDRL) technique to enhance QoS in IoT. Our approach involves collecting and preprocessing data samples before storing them in the IoT cloud and monitoring user access. We evaluate our framework using metrics such as packet loss, throughput, processing delay, and overall system data rate. Our results show that our developed framework achieved a maximum throughput of 94%, indicating its effectiveness in improving QoS. We believe that our deep learning optimization approach can be further utilized in the future to enhance QoS in IoT platforms

Automated telemedicine and diagnosis system (ATDS) in diagnosing ailments and prescribing drugs

This paper presents an Automated Telemedicine and diagnosis system (ATDS), is an expert system that is used for diagnosing many ailments such as malaria and others. and prescription of drugs. ATDS is made up of knowledge database. The end user makes use of it to determine if they are suffering from one ailment or the by entering the ailment symptoms. Once the system receives the information from the user, an instant drug prescription and diagnosis is made. ATDS is user-friendly. However, ATDS was not developed to replace the real doctors but to assist patient waiting for human doctors that are few and to make the diagnosing and prescribing of drugs faster and more accurate. ATDS is implemented in PHP (Hypertext Preprocessor). Hospitals and other medical clinics should be readily accessible by everyone when needed as one of the many limitations of health care establishment is distance. ATDS when compared with old systems like MYCIN is more efficient in the different aspects. It solved the problem of incomplete base. ATDS proves efficiency more than another medical expert system by giving instant and correct ailment diagnosis and drug prescription

A survey on short-range WBAN communication; technical overview of several standard wireless technologies

In a healthy environment, a WBAN system is the key component or aspect of the patient monitoring system. WBAN systems allow for easy networking with other devices and networks so that healthcare professionals can easily access critical and non-critical patient data. One of the main advantages of WBAN is the remote monitoring of patients using an Intranet or the Internet. There are two main components to the type of communication technology used in WBAN. This page shows an insight of a variety of short-range standardized wireless devices, as well as a taxonomy of short-range technologies. These are proposed as intra-BAN communication candidates for communication within and between body area network (BAN) entities. This paper also highlights the advantages and disadvantages of the WBAN perspective. Finally, a side-by-side comparison of the basic principles of using MICS frequency bands and preparatory technologies

Lung cancer classification using data mining and supervised learning algorithms on multi-dimensional data set

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about the cancer disease detection. Developing a proposed data mining model is useful to diagnose the cancer disease once the cancer detection is accomplished using data mining for the examination and classification of machine learning supervised algorithms