IMPROVING PET SCANNER TIME-OF-FLIGHTRESOLUTION USING ADDITIONAL PROMPT PHOTON

Positronium Imaging requires two classes of events: double-coincidences originated from pair ofback-to-back annihilation photons and triple-coincidences comprised with two annihilation photonsand one additional prompt photon. The standard reconstruction of the emission position along theline-of-response of triple-coincidence event is the same as in the case of double-coincidence event;an information introduced by the high-energetic prompt photon is ignored. In this study, we proposeto extend the reconstruction of position of triple-coincidence event by taking into account the timeand position of prompt photon. We incorporate the knowledge about the positronium lifetime distributionand discuss the limitations of the method based on the simulation data. We highlight that theuncertainty of the estimate provided by prompt photon alone is much higher than the standard deviationestimated based on two annihilation photons. We finally demonstrate the extent of resolutionimprovement that can be obtained when estimated using three photons

Character/Word Modelling: A Two-Step Framework for Text Recognition in Natural Scene Images

Text recognition from images is a complex task in computer vision. Traditional text recognition methods typically rely on Optical Character Recognition (OCR); however, their limitations in image processing can lead to unreliable results. However, recent advancements in deep-learning models have provided an effective alternative for recognizing and classifying text in images. This study proposes a deep-learning-based text recognition system for natural scene images that incorporates character/word modeling, a two-step procedure involving the recognition of characters and words. In the first step, Convolutional Neural Networks (CNN) are used to differentiate individual characters from image frames. In the second step, the Viterbi search algorithm employs lexicon-based word recognition to determine the optimal sequence of recognized characters, thereby enabling accurate word identification in natural scene images. The system is tested using the ICDAR 2003 and ICDAR 2013 datasets from the Kaggle repository, and achieved accuracies of 79.8% and 81.5%, respectively

CHARACTERISTIC SKY BACKGROUND FEATURES AROUND GALAXY MERGERS

In the context of finding galaxy merger in large-scale surveys, we applied MachineLearning algorithms that, instead of using the images as it is the currentstandard, made used of flux measurements. Training multiple NNs using aclass-balanced dataset of mergers and non-mergers Sloan Digital Sky Survey,we found that the sky background error parameters could provide a validation92.64 ± 0.15 % accuracy of and a training accuracy of 92.36 ± 0.21 %.Moreover, analysing the NN identifications led us to find that a simple decisiondiagram using the sky error for two flux filters is enough to get a 91.59 % accuracy.By understanding how the galaxies vary along the diagram, and trying toparametrize the methodology in the deeper images of the Hyper Suprime-Cam,we are currently trying to define and generalize this sky error-based methodology

PREFACE: 2ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONSIN MEDICINE AND PHYSICS

This is a preface for the special issue including extended versions of the selected papers submitted to 2ND INTERNATIONAL WORKSHOP ON MACHINE LEARNING AND QUANTUM COMPUTING APPLICATIONSIN MEDICINE AND PHYSICS

Eye Disease Segmentation using Hybrid Neural Encoder Decoder based Unet Hybrid Inception

Diabetic retinopathy (DR) is one of the major causes of vision problems worldwide. With proper treatment, early diagnosis of DR can prevent the progression of the disease. In this paper, we present a combinative method using U-Net with a modified Inception architecture for the diagnosis of both the diseases. The proposed method is based on deep neural architecture formalising encoder decoder modelling with convolutional architectures namely Inception and Residual Connection. The performance of the proposed model was validated on the IDRid 2019 contest dataset. Experiments demonstrate that the modified Inception deep feature extractor improves DR classification with a classification accuracy of 99.34% in IDRid across classes with comparison to Resnet. The paper Benchmark tests the dataset with proposed model of Hybrid Dense-ED-UHI: Encoder Decoder based U-Net Hybrid Inception model with 15 fold cross validation. The paper in details discusses the various metrics of the proposed model with various visualisation and multifield validations

A Physical Model of Quantum Bit Behavior Based on a Programmable FPGA Integrated Circuit

The rapidly developing field of quantum computing and the ongoing lack of widely available quantum computers create the need for scientists to build their simulators. However, mathematical simulation of such circuits usually ignores many aspects and problems found in real quantum systems. In this article, the authors describe a quantum bit emulator based on FPGA integrated circuits. In this case, FPGA technology provides real-time massive parallelism of the modeled physical phenomena. The modeled QUBIT is represented using a Bloch sphere. Its quantum state is set and modified only by precise pulses of an electrical signal, and with the help of similar pulses, it manifests its current state in real time. The constructed QUBIT was additionally equipped with decoherence mechanisms and with circuits that intentionally respond to internal and external noises that distort its current quantum state. This article presents and discusses how such a physically built emulator works

PRELIMINARY STUDY ON ARTIFICIAL INTELLIGENCE METHODS FOR CYBERSECURITY THREAT DETECTION INCOMPUTER NETWORKS BASED ON RAWDATA PACKETS

Most of the intrusion detection methods in computer networks are based ontraffic flow characteristics. However, this approach may not fully exploit thepotential of deep learning algorithms to directly extract features and patternsfrom raw packets. Moreover, it impedes real-time monitoring due to the neces-sity of waiting for the processing pipeline to complete and introduces depen-dencies on additional software components.In this paper, we investigate deep learning methodologies capable of de-tecting attacks in real-time directly from raw packet data within network traffic.Our investigation utilizes the CIC IDS-2017 dataset, which includes both benigntraffic and prevalent real-world attacks, providing a comprehensive foundationfor our research

An improved context-aware Sentiment Analysis of student comments on Social Networks based on ChatGPT

The widespread use of social networks has provided a variety of active, dynamic, and popular platforms for students to express their opinions and sentiments. These data are increasingly being exploited and integrated into university information systems to better govern and manage universities and improve educational quality. The analysis of such data can offer valuable insights into student experiences and attitudes towards various educational aspects including courses, professors, events, and facilities. However, automatic opinion mining in this context is challenging due to the difficulty of analyzing some languages such as Arabic, the variety of used languages, the presence of informal language, the use of emoticons and emoji, sarcasm, and the need to consider the surrounding context. To deal with all these challenges, we propose a novel approach for an effective sentiment analysis of student comments on the X platform (Twitter). The proposed approach allows to collect student comments from Twitter public pages and automatically classifies comments into positive, negative, and neutral. The new approach is based on ChatGPT capabilities, supports three languages: English, Arabic, and colloquial Arabic, and integrates a new scoring method that measures both the positiveness and subjectivity of student comments. Experiments performed on simulated and real public Twitter pages of five Saudi high education institutions showed the performance of the proposed tool to automatically analyze and summarize collected data

Energy Efficient and QoS Aware Trustworthy Routing Protocol for Manet Using Hybrid Optimization Algorithms

The potential for wireless mobile computing applications has significantly increased in recent years thanks to advancements in wireless communication and internet service technology. A collection of mobile nodes that can be randomly arranged and created without the aid of any pre-existing network architecture or centralised administration is known as a Mobile Ad hoc Network (MANET). Mobile devices in this network rely on battery power, so it\u27s critical to reduce their energy usage. Furthermore, communication with them is difficult due to their susceptibility to several security risks. As a result, the research suggested a reliable routing protocol that is both energy-efficient and QoS-aware. Levy Flight-centred Shuffled Shepherd Dynamic Source Routing (LF-SSO-DSR) protocol is used in the route discovery scheme\u27s first stages to find the best way out of a group of options chosen based on QoS criteria. Additionally, hybrid Firefly and Whale Optimization Algorithms (FFWHO) are used to handle high energy consumption and discover the ideal values and fit function for the goal parameter (i.e., energy). WOA conducts a global search, but later on, in the algorithm, it conducts a local search, which can successfully find the routing path that complies with the QoS requirements. The security challenges in MANETS present the most difficult assignment. The reliability of each mobile node is assessed by taking into account factors such as the node\u27s location, mobility speed, energy use, number of involved transmissions, neighbour list, etc. The research project then suggested the Intelligent Dynamic Trust (IDT) paradigm as a means of supplying security in wireless networks. For secure routing in mobile ad hoc networks, this paradigm combines beta reputation trust and dynamic trust. Network Simulator 3.36 software was used to conduct the performance analysis. Several performance metrics, including throughput, energy consumption, packet delivery ratio, jitter, end-to-end delay, packet loss rate, detection rate, and routing overhead, are used to assess the suggested approach. This outcome shows that the suggested strategy works better than other cutting-edge approaches respectively

A Proposal of Digital Contents Copyright Protection by using Blockmarking Technique

Recently, blockmarking technique \cite{blockmarking} is proposed for a new hybrid model based on the combination of blockchain and watermarking method. In this model, it not only achieves the goal of image copyright protection but also stores the image into the blockchain network such as IPFS system. In this paper, we propose a new DRM system by inheriting the idea of blockmarking. The copyright contents can be distributed via IPFS blockchain, then be restored by using the reconstruction license for each legal user. Also, in our method, based on the reconstruction licenses, the distributed contents can be reconstructed from IPFS with various watermarking patterns. It helps us can manage the legal users and trace the traitor if a dispute occurs. The experimental results show that our method successfully achieved the purpose of digital copyright protection