Authors - Abdullah I Alshoshan Abstract - Verification and/or identification (VI) of the individual writer-identity is one of the most common secure personal biometric authentications, particularly in banks for verification and in sensitive data storages for both VI. A writer-identity VI system (WIVIS) is proposed using the writer-invariant features of his/her script using two approaches: offline approaches, which rely on the script information in a static format, such as an image or shape, and online approaches, which require the collection of information in a dynamic format, such as speed and acceleration, using a tablet with a stylus pen to capture both of these dynamic information. Both offline script VI methods, such as normalized Fourier transform descriptor (NFTD) and normalized central moment (NCM), and online script VI methods, such as normalized script speed and acceleration, will be discussed. These features are compared individually and then as a combination. In the combination mode, the neural network (NN) is used for classification. Implementation and testing of the WIVIS is done and analyzed, and the effectiveness of each invariant algorithm, regardless of the language or form of the script shape, is discussed. A set of data on the online (dynamic) and offline (static) script is also discussed.
Authors - Dzung Lai Ngoc, Maria Luojus, Jukka Heikkonen, Rajeev Kanth Abstract - The development of cancer therapeutic therapies has made significant advancements in recent years. Numerous innovative solutions have emerged, achieving notable success, including immunotherapy, targeted drugs, and, among them, oncolytic viruses. Oncolytic virus therapy represents the first instance in which humans have employed a biological logic program, rather than a conventional drug, to treat a disease. Despite its promising potential, clinical trials involving oncolytic viruses have not yielded the anticipated outcomes, due to our incomplete understanding of the underlying biological logic and mechanisms. This paper will describe a treatment approach from a biological algorithmic standpoint, encompassing biological logic programs, molecules that carry biological logic (Logical Biomolecular Complexes - LBC), and the existing tools that can be used to design such treatment programs. Our proposal is based on a review of oncolytic virus studies, but the logic framework behind LBCs is tailored specifically for cancer treatment, rather than focusing on replication and spreading. This sets LBCs apart from their viral counterparts and can be considered a new concept.
Authors - Julian Andres Duarte Suarez, Leonardo Juan Ramirez Lopez Abstract - Given the continuous need of beds available for hospitalization in health institutions, and even more so during pandemic periods, it is necessary to have alternatives that allow patients to be transferred to their homes and from there to carry out continuous monitoring of their health. For this, DATALOG was developed, which is an Internet of Medical Things platform that acquires, processes, transmits, stores and manages the medical signals of patients from their home to a central hospital. Following this, the stored data is processed by applying the Standard Intersectoral Process methodology for data mining that al-lows them to provide the medical staff with the behavior of six physiological variables and visualize it in a unique control table developed in php. Initial tests show an acceptance of the medical staff as a service and support tool for medical decisions, and, in addition, it has been proven that hospitalization at home con-tributes significantly to the rapid improvement of the patient, thus decongesting the hospital system. Among the most recent innovations, DATALOG includes an early warning system that allows to warn about the patient's condition between normal, attention, alert and critical, with the possibility of sending the alert to mobile systems. It is concluded that DATALOG is a useful service and tool for e-health, and machine learning techniques allow for the prediction of patient states and alerts.
Authors - K P N S Dayarathne, U Thayasivam Abstract - Deep learning has achieved amazing success in multiple areas, such as Image Classification, speech recognition, Object Detection & Segmentation, natural language processing, audio-visual recognition, adaptive testing, etc., and is gaining major interest from the research community. The application for deep learning is growing day by day. Predicting interest rate as univariate analysis is important given that total spectrum of the interest rates is not available to apply yield curve analysis. This paper investigates the applicability of deep learning models such as RNN, LSTM, CNN and TCN to interest rates in Asina frontier countries such as Sri Lanka, Pakistan and Bangladesh. The deep learning approach for interest rate perdition is still under the radar, and this is the first attempt on the Asian Fronter market. Interest rates associates with Government securities were considered to have uniqueness for all three countries, where data range from 2010-2022 for Sri Lanka and Pakistan, whereas Bangladesh analysis was based on the same from 2015-2022. The results revealed CNN was the best model for Sri Lanka and Bangladesh, while LSTM was the best model for Pakistan based on the lowest RMSE. The study further investigates the applicability of different activation function for output layers and hidden layers, but found ReLU is the most viable activation function along with Max pooling. Further, it was found that CNN works better for countries with stable term structures of interest rates, and the immediate dynamics of the interest rate influence the near future interest rate.
Authors - Taeko Onodera, Koutaro Hachiya, Yuhei Hatakenaka Abstract - Numerous studies have applied machine learning to diagnosis and screening in the medical and welfare fields. However, it is rare for the resulting machine learning models to be widely adopted in clinical practice. This study proposes a method for deriving diagnostic rules from machine learning models that can be applied manually without the use of computers. The proposed method involves inputting all possible patterns into a trained model, generating a truth table with the corresponding prediction results, and then using the Quine– McCluskey method to derive logical expressions that serve as manual diagnostic rules. In the experiments, the proposed method was compared with conventional methods for deriving manual diagnostic rules from datasets: the point score system, a method based on likelihood ratios, and a logic derivation method based on rough set theory. Only the proposed method achieved a positive clinical utility index of 0.81 or higher—classified as “excellent”—even when the number of rules was limited to just two or three.
Authors - El Hadji Bassirou TOURE, Ibrahima FALL, Mandicou BA, Alassane BAH Abstract - Microservice architectures offer scalability and deployment benefits but introduce significant data consistency challenges due to distributed data ownership and the necessity of data duplication across services. Current approaches either compromise service autonomy with strong consistency mechanisms or shift complexity to developers. This paper presents Micro-Flex, a novel megamodel-based consistency management framework that treats data entities as component models with formally tracked consistency states.We extend the Modified-Shared-Invalid protocol with differentiated Shared states (Shared+ and Shared−) to accommodate varying consistency requirements while maintaining formal guarantees. Our approach formalizes Global Operation Models (GOMs) as application specifications where consistency states are actively managed through well-defined transition rules. We validate Micro-Flex through an e-commerce case study, demonstrating its effectiveness in balancing consistency guarantees with service autonomy while addressing data duplication challenges. 4 By applying Model-Driven Engineering principles to distributed consistency challenges, our framework contributes to more disciplined data management in microservice architectures.
Authors - Nikolaos Lazaropoulos, Ioannis Vaxevanakis, Ioannis Sigalas, Ioannis Lamprou, Vassilis Zissimopoulos Abstract - Cloud Native Functions (CNFs) support automated and dynamic orchestration of containerized network services, replacing traditional hardware-based architectures. These deployments consist of modular microservices that enable elastic scalability and collaborative service delivery. This paper presents an approximation framework for capacity constrained CNF resource allocation, modeled as variants of the Group Generalized Assignment Problem (Group GAP). The main contributions are: (1) a 1 2 -approximation algorithm for CNF placement when each function’s footprint is at most half the cluster capacity and (2) a 1 2 (1 − e−1/d)-approximation for shared microservices among multiple CNFs, where d is the degree of sharing, supported by experimental evaluation of the algorithm relative error.
Authors - Abdulaziz Alkhajeh, Sara Alhashmi, Alya Al Ali, Rakan Alhosani, Suhail Alshehhi, Deepa Pavithran, Joseph Anajemba Abstract - Healthcare has always been a crucial part of human life, with people investing resources to get the best services available. Ensuring patient confidentiality has always been crucial, but the digital era introduces new security risks. Hospitals now store patient information in computerized databases, which are vulnerable to cyberattacks. One major threat is ransomware attacks, where hackers capture sensitive and confidential patient data and demand large sums of money to prevent it from being leaked or sold. This puts patient privacy at risk and can disrupt healthcare services. Also, unauthorized access to the patients’ information compromising the data confidentiality has been a growing concern because health care has always been sensitive and personal information that should not be utilized for commercial purposes. Blockchain technology offers a solution by providing a secure way to store patient files. Using an Interplanetary File System (IPFS) on the blockchain, healthcare providers can save patient records in a decentralized and protected system, reducing the risks linked to traditional databases. This method helps protect patient information from cyber threats, ensuring privacy and security. In this paper, we are using blockchain-based architecture coupled with pinata IPFS cloud to secure the patient’s valuable information from any kind of cyber-attack, including ransomware.
