Smart, Autonomous and Reliable Internet of Things

AbstractThe dynamic rapidly changing and technology-rich digital environment enables the provision of added-value applications that exploit a multitude of devices contributing services and information. As the Internet of Things (IoT) techniques mature and become ubiquitous, emphasis is put upon approaches that allow things to become smarter, more reliable and more autonomous. In this paper we present challenges and enablers as technologies that will allow things to evolve and act in a more autonomous way, becoming more reliable and smarter. We describe decentralized management mechanisms targeting IoT-based systems in order to enable the exploitation of millions of devices, while we also present an architecture that allows things to learn based on others experiences. The proposed architectural approach also introduces situational knowledge acquisition and analysis techniques in order to make things aware of conditions and events affecting IoT-based systems behavior

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PD

5G & SLAs: Automated proposition and management of agreements towards QoS enforcement

Efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become mandatory to guarantee the required service quality in software- defined and 5G networks. To create an operational Network Service, it is highly envisaged to associate it with their network-related parameters that reflect the corresponding quality levels. These are included in policies but while SLAs target usually business users, there is a challenge for mechanisms that bridge this abstraction gap. In this paper, a generic black box approach is used to map high-level requirements expressed by users in SLAs to low-level network parameters included in policies, enabling Quality of Service (QoS) enforcement by triggering the required policies and manage the infrastructure accordingly. In addition, a mechanism for determining the importance of different QoS parameters is presented, mainly used for “relevant” QoS metrics recommendation in the SLA template

A Comparative Study of Collaborative Filtering in Product Recommendation

Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PD

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PD

Transforming Sentiment Analysis in the Financial Domain with ChatGPT

Financial sentiment analysis plays a crucial role in decoding market trends and guiding strategic trading decisions. Despite the deployment of advanced deep learning techniques and language models to refine sentiment analysis in finance, this study breaks new ground by investigating the potential of large language models, particularly ChatGPT 3.5, in financial sentiment analysis, with a strong emphasis on the foreign exchange market (forex). Employing a zero-shot prompting approach, we examine multiple ChatGPT prompts on a meticulously curated dataset of forex-related news headlines, measuring performance using metrics such as precision, recall, f1-score, and Mean Absolute Error (MAE) of the sentiment class. Additionally, we probe the correlation between predicted sentiment and market returns as an additional evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment analysis model for financial texts, exhibited approximately 35\% enhanced performance in sentiment classification and a 36\% higher correlation with market returns. By underlining the significance of prompt engineering, particularly in zero-shot contexts, this study spotlights ChatGPT's potential to substantially boost sentiment analysis in financial applications. By sharing the utilized dataset, our intention is to stimulate further research and advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with Application

Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis

Risk Factors of Pancreatic Cancer: A Literature Review

Objective: To identify and compare current modifiable and non-modifiable risk factors for pancreatic cancer (PaCa) that may have potential application in PaCa risk stratification, prevention, and early detection. / Material: All articles in this literature review were identified through systematic searches of PubMed, Medline, and Embase databases. All articles were published in the English language, between January 2000 to December 2021 and with an abstract. In this review study, we judge the evidence level of different PaCa risk factors through the criteria of grading evidence for cancer prevention. / Results: The modifiable risk factors identified included cigarette smoking, heavy alcohol consumption, increased Body Mass Index (BMI) and abdominal obesity, chronic pancreatitis, diabetes, hepatitis B virus (HBV) infection, periodontal disease, cholecystectomy and chemicals and asbestos exposure. The non-modifiable risk factors included age, gender, ethnicity, blood type, family history, inherited syndromes, germline mutation, and single nucleotide polymorphisms (SNPs). However, there are still some ambiguous risk factors for which the current evidence is inconclusive such as low physical activity, increased consumption of red/processed meat and dairy products, vitamin D insufficiency, and some medical-related conditions including gut microbiota such as Helicobacter Pylori infection, long-term usage of Proton-pump inhibitors (PPI), and Systemic Lupus Erythematosus (SLE) history. / Conclusions: This literature review summarizes the modifiable and non-modifiable risk factors of PaCa with strong evidence, which could be used to further establish PaCa predictive model as an application of PaCa risk stratification, raise public awareness and educate the public as a prevention program. Further studies are needed to investigate other potential risk factors

Challenges Emerging from Future Cloud Application Scenarios

The cloud computing paradigm encompasses several key differentiating elements and technologies, tackling a number of inefficiencies, limitations and problems that have been identified in the distributed and virtualized computing domain. Nonetheless, and as it is the case for all emerging technologies, their adoption led to the presentation of new challenges and new complexities. In this paper we present key application areas and capabilities of future scenarios, which are not tackled by current advancements and highlight specific requirements and goals for advancements in the cloud computing domain. We discuss these requirements and goals across different focus areas of cloud computing, ranging from cloud service and application integration, development environments and abstractions, to interoperability and relevant to it aspects such as legislation. The future application areas and their requirements are also mapped to the aforementioned areas in order to highlight their dependencies and potential for moving cloud technologies forward and contributing towards their wider adoption