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AGH University of Krakow, Faculty of Computer Science
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.
Copyright (c) 2025 Computer Science https://creativecommons.org/licenses/by/4.0
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 on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from 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 benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.
CHARACTERISTIC SKY BACKGROUND FEATURES AROUND GALAXY MERGERS
In the context of finding galaxy merger in large-scale surveys, we applied Machine Learning algorithms that, instead of using the images as it is the current standard, made used of flux measurements. Training multiple NNs using a class-balanced dataset of mergers and non-mergers Sloan Digital Sky Survey, we found that the sky background error parameters could provide a validation 92.64 ± 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 decision diagram 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 to parametrize the methodology in the deeper images of the Hyper Suprime-Cam, we are currently trying to define and generalize this sky error-based methodology.
DEVELOPING ARTIFICIAL INTELLIGENCE IN THE CLOUD: THE AI INFN PLATFORM
The INFN CSN5-funded project AI INFN (“Artificial Intelligence at INFN”) aims to promote ML and AI adoption within INFN by providing comprehensive support, including state of-the-art hardware and cloud-native solutions within INFN Cloud. This facilitates efficient sharing of hardware accelerators without hindering the institute’s diverse research activities. AI INFN advances from a Virtual-Machine-based model to a flexible Kubernetes-based platform, offering features such as JWT-based authentication, JupyterHub multitenant interface, distributed file system, customizable conda environments, and specialized monitoring and accounting systems. It also enables virtual nodes in the cluster, offloading computing payloads to remote resources through the Virtual Kubelet technology, with InterLink as provider. This setup can manage workflows across various providers and hardware types, which is crucial for scientific use cases that require dedicated infrastructures for different parts of the workload. Results of initial tests to validate its production applicability, emerging case studies and integration scenarios are presented.
A NETWORK-BASED COMPUTATIONALPIPELINE TO STUDY THE VARIABILITY OFTRANSCRIPTOME PROFILES FOR HUMANDISEASES
Machine learning applications to high-throughput data in medicine – one of the biggest resource for understanding complex disease – so far have been limited. Here we present a computational approach for assessing the intrinsic variability in the most prominent data type, transcriptomics data for disease cohorts. Our study looks at situations, where multiple datasets for the same disease are available. We leverage concepts of network medicine to assess, how the match between a biological network and a set of differentially expressed genes varies across different networks and experiments. Our results show that different biological networks yield markedly different results. Also, the clustering of diseases depends strongly on the choice of parameters contained in the data analysis and network processing.
AQMLATOR – AN AUTO QUANTUMMACHINE LEARNING E-PLATFORM
A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on automatic architecture search — a meta method that aims at removing human from ML system design process. The success of ML and the development of quantum computing (QC) in recent years led to a birth of new fascinating field called Quantum Machine Learning (QML) that, amongst others, incorporates quantum computers into ML models. In this paper we present AQMLator, an Auto Quantum Machine Learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. This way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.
RECONSTRUCTION OF MUON BUNDLES IN KM3NET DETECTORS USING MACHINE LEARNING METHODS
The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.
IMPROVING PET SCANNER TIME-OF-FLIGHTRESOLUTION USING ADDITIONAL PROMPT PHOTON
Positronium Imaging requires two classes of events: double-coincidences originated from pair of back-to-back annihilation photons and triple-coincidences comprised with two annihilation photons and one additional prompt photon. The standard reconstruction of the emission position along the line-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 propose to extend the reconstruction of position of triple-coincidence event by taking into account the time and position of prompt photon. We incorporate the knowledge about the positronium lifetime distribution and discuss the limitations of the method based on the simulation data. We highlight that the uncertainty of the estimate provided by prompt photon alone is much higher than the standard deviation estimated based on two annihilation photons. We finally demonstrate the extent of resolution improvement that can be obtained when estimated using three photons.
Modified honey bee algorithm with random selection of virtual machines for dynamic load balancing
Cloud workloads can overwhelm load balancers, leading to inefficiencies and performance issues. To address these challenges, the Honey Bee Load Balancing algorithm is highly effective in enhancing cloud resource allocation. Inspired by the foraging behavior of honey bees, this algorithm offers a dynamic approach to resource distribution, adapting to changing workloads in real-time. This paper delves into the key features and advantages of Honey Bee Load Balancing, focusing on its dynamic resource allocation, overall response time, and data center processing time. Through a comparative study of existing methodologies, we propose a modified Honey Bee Load Balancing algorithm that incorporates the random selection of virtual machines. Utilizing the CloudAnalyst tool for simulation, we compare traditional and proposed Honey Bee Load Balancing algorithms to evaluate overall response time and data center processing time. The proposed algorithm demonstrates superior performance in these parameters compared to the traditional approach.
Developing explainablemachine learning model usingaugmented concept activation vector
Machine learning models use high-dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model’s decisions.
Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using Multi-Layer Perceptron and LSTM
Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques help improve the diagnostic accuracy of Parkinson’s disease progression. In this research work, two well-known feature selection methods (Relief-F and Sequential Forward Selection) were employed to identify the diagnostic features of audio signals of Parkinson disease patients and were used to train Multi-Layer Perceptron (MLP) and recurrent neural network Long Short-Term Memory(LSTM) for detection of disease and prediction of its progression. The MLP accurately detected Parkinson disease stages whereas LSTM successfully predicted Parkinson Stage 2 and 3.
GDPKG-LLM: Integrating Gene, Disease, and Pharmacogenomics Knowledge Graphs for Cognitive Neuroscience Using Large Language Models
Using the structures of large language models (LLMs) in creating knowledgediagrams to understand more about the relationship between the entities ofcognitive and biological sciences has become a hot point of research. Due to thegreat knowledge behind the curtain and the deep connections of this research,it is not possible to use the traditional approaches of machine learning and deeplearning. In this study,the main goal is to create a comprehensive and integratedknowledge graph(KG) from the combination of three knowledge sources: GeneOntology (GO), Disease Ontology (DO), and PharmKG. Large language models(LLMs) have been used to create this knowledge base. The main purpose ofthis KG is to understand the relationships between genes, diseases and drugs.The pro- posed approach was called GDPKG-LLM. It has several key steps,including entity matching, similarity analysis, graph alignment and using GPT-4. GDPKG-LLM was able to extract more than 16,800 nodes and 838,000 edgesfrom these three knowledge bases and provide a rich KG. This graph providesmeaningful relationships, making it a valuable resource for future research inpersonalized medicine and neuroscience. The reviewed evaluation criteria showthe superiority of GDPKG-LLM, which strengthens the validity of this model.
This review deviates from the usual approach to the topic of information by not focusing on Shannon’s Theory of Communication (TOC) and the related or derived concepts. In addition, we do not talk at length about information in relation to knowledge, data, communication, information processing, or similar concepts. Instead, we endeavor to reappraise our understanding of information without favoring any specific perspective. We know a lot about information, and the various conceptualizations of information presented in this paper are proof of this. Nevertheless, we also show that some lingering unresolved questions remain about the nature of information. To somewhat stem the appearance of further new concepts of information, we consider two perspectives, namely ontological and epistemic, and posit that we can potentially reduce all information variants to just these concepts. We then look at two general theories of information: the General Definition of Information (GDI) and the General Theory of Information (GTI), arguing that the GTI appears to be the better of these two options because it is more fundamental and comprehensive with deep metaphysical roots. Finally, we review some recent studies about information’s physical nature, such as for information and mass, meaningful physical information, and the persistence of information. This review, like all reviews, is selective and synthetic, but the extensive reference list provides the necessary resources to explore the discussed ideas in greater detail, as well as study the recent works on the nature of information.
Anaphora solved Ad-Dl-Bert model for text summarization with auto encoding using the topic description and several priors (ATDS) approach
Owing to the large amount of digital text content in articles, novels stories, and so on, Automatic Text Summarization (ATS) is becoming a significant task. Abstractive or extractive summaries of single or multi documents have been generated by various researchers. Although several models were generated, there are still limitations like the anaphora problem that occurred during the summarization. To overcome such limitations, this paper proposes the Added dropout-Deleted Layer norm-Bidirectional Encoder Representations from Transformers (Ad-DL-BERT)-based Extractive Text Summarization (ETS). Primarily, the input document’s sentences are prepared for accurate summarization by pre-processing; then, the unwanted sentences are removed. Afterward, with the Auto encoding using the Topic Description and Several priors (ATDS) approach, the sentences under the same topic are clustered. Moreover, keywords for summarization are extracted with an Anaphora-POS (An-POS) extractor. Thereafter, for removing the redundant sentences, the ranking with Exponential Linear Unit-Generative Adversarial Network (ELU-GAN) and saliency score assignment processes are performed. Also, assignments for sentences are performed to enhance the coherency, sorting, and cosine similarity score. Lastly, the Ad-DL-BERT generated summary and the proposed technique’s performance are evaluated on the Document Understanding Conference (DUC2002) dataset. Regarding clustering time, execution time, Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1) scores of recall, F-measure, and precision, the experimental outcomes exhibited the proposed techniques’ dominance over the conventional approaches.
Special Section - Natural language processing for intelligent modelling
Intrusion detection using federated learning with neural networks
The amount of information shared amongst different devices and the variety of novel methods of network crimes have exponentially increased in recent years because of the widespread use of the internet. Quick identification of all types of attacks would not be possible with conventional methods including firewalls, which focused on data filtering. Dealing with the timely recognition of these types of assaults is very successful for intrusion detection systems (IDS) grounded on ML algorithms. They can efficiently manage the enormous amount of data in order to identify any harmful behaviour. Every network activity is searched for any possibly dangerous activity using IDS based on machine learning. The main objective of the planned effort is to provide analytical analyses of such current intrusion detection systems. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investigated under several criteria in line to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IOT 2023 Dataset is the one applied in this paper. Tested were several techniques including random forest, XG Boost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy—99.84%.
Optimized lossless audio compression using DCT energy thresholding and machine learning technique
In this paper, a novel lossless audio compression technique has been proposed, utilizing the Discrete Cosine Transform (DCT) coefficient-controlled technique based on energy thresholding, an XOR-based neural network compression model, and a CNN model. Initially, the DCT is applied to the input audio signal to achieve better energy compaction, followed by transforming selected DCT coefficients into a compressed binary stream. Subsequently, this binary stream is passed to two prediction-based optimized models: an XOR model and a CNN model for further compression. The binary stream is first processed by the neural network model for XOR operation, and the resulting output is then fed into a CNN model to reduce data dimensionality and generate compressed audio data. The simulation findings are analyzed using various statistical and robustness measures and compared with existing approaches.
Stable and low associative left-right hashing
Hashing is indispensable for efficient search operations, captivating the interest of numerous researchers. Among the diverse array of techniques, Cuckoo Hashing has emerged as particularly effective across a wide range of applications. It is implemented in two primary forms: Parallel Cuckoo Hashing and Sequential Cuckoo Hashing. Nonetheless, Cuckoo Hashing encounters significant challenges, including high insertion latency, inefficient memory usage, and high data migration costs. The concept of Combinatorial Hashing has inspired this research. Our proposed scheme enhances Combinatorial Hashing and introduces an innovative collision resolution technique called Left-Right Random Probing. This advanced variant of random probing strategically utilizes prime numbers and Fibonacci sequences to improve performance. This paper introduces two performance indicators, the degree of dexterity and table reference count per key. This paper identifies switching cost as a new challenge in Cuckoo Hashing and quantifies this switching cost using the parameter, table reference count per key.
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.
Copyright (c) 2024 Computer Science https://creativecommons.org/licenses/by/4.0
Using Splitter Ordering Heuristics to Improve Bisimulation in Probabilistic Model Checking
Model checking is used to verify computer-based and cyber-physical systems, but faces challenges due to state space explosion. Bisimulation minimization reduces states in transition systems, easing this issue. Probabilistic bisimulation further simplifies models with stochastic behaviors. Recent techniques aim to improve time complexity of iterative methods in computing probabilistic bisimulation for stochastic systems with nondeterministic behaviors. In this paper, we propose several techniques to accelerate iterative processes to partition the state space of a given probabilistic model to its bisimulation classes. The first technique applies two ordering heuristics for choosing splitter blocks. The second technique uses hash tables to reduce the running time and the average time complexity of the standard iterative method. The proposed approaches are implemented and run on several conventional case studies and reduce the running time by one order of magnitude on average.
EResNeXt: A multiplerepresentation and attention based technique for fingerprint presentation attackdetection in unknown presentation attack instruments scenario
Fingerprint biometrics are one of the most common authentication mechanisms. However, such systems are often compromised by presentation attacks by presentation attack instruments. Most of the fingerprint presentation attack detection approaches show poor performance due to the large variation in presentation attack instruments and limited feature representation of input fingerprint. Therefore this article proposes a hybrid model of shallow and deep features with multiple representations of input fingerprints. To obtain these shallow and deep features first we have enhanced the texture of the input fingerprint through a novel median adaptive local binary pattern filter and existing binarised statistical image feature. After that, the input fingerprint image and two textured enhanced images are concatenated along with the channel dimension for multiple representations. Finally, an extended ResNeXt architecture with channel and spatial attention (EResNeXt) has been used for relevant feature extraction and presentation attack detection. The proposed model (EResNeXt) has been assessed on LivDet-2015 and Livdet-2017 datasets and provides significant results in unknown presentation attack instrument scenarios.
Automatic indexation of Cultural Heritage 3D object
There has been significant evolution in the fields of 3D digitization thanks to the development of 3D reconstruction and geometry processing. The results of digitization researches have been widely applied in many fields, especially in Cultural Heritage and Archaeology. Reconstruction, characterization and annotation of components forming 3D objects have become an effective tool for research, conservation and promotion of archaeological relics. The aim of this paper is to propose a process of 3D model reconstruction, segmentation and annotation on the basis of a enhanced corresponding 2D dataset. A machine learning method is used for the semantic segmentation of 2D images, thereby label, annotate and reconstruct a 3D model based upon links between distinctive invariant features, orientation of images, and depth map of images. The initial result as a data basis for research, reconstruction and identification of parts in 3D objects is applied in the reconstruction of archaeological relics, object identification, 3D printing, etc. Our work uses the datacollected from the Museum of Cham Sculpture – DaNang and the Myson QuangNam sanctuary in VietNam, to carry out the proposed method.
The Ant Colony Optimization Algorithm Applied in Transport Logistics
The Vehicle Routing Problem belongs to graph optimization and its goal is to find shortest routes visiting a given set of customers with additional constraints present. The article presents the ant colony optimization metaheuristic which solves vehicle routing problems and its real-life application in transport logistics (finding routes for delivery companies). The metaheuristic generated high-quality solutions (superior to compared methods). Our tool is flexible and enables us to solve various variants of routing problems so it is well suited to specific needs of transportation companies.
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.
The Power of Intelligence Emerging from Swarms
Swarm intelligence (SI) is a field of study that seeks to understand and model collective behaviors observed in natural social systems, such as ant colonies, bee hives, termite mounds, flocks of birds or schools of fish. The central principle of SI is that complex intelligent behaviors can emerge from the interactions of large numbers of simple individual entities, without any centralized control or monitoring. SI researchers aim to uncover the underlying principles and mechanisms behind this SI, with the aim of applying these concepts to solve complex problems in areas such as optimization, robotics, transport, IT, etc. As the field continues to evolve, SI is expected to have an increasingly significant impact on our understanding of biological systems and our ability to design intelligent systems capable of adapting and thriving in complex environments and dynamic. This article aims to introduce the reader to the field of SI, presenting its fundamental concepts, key principles, existing applications, and prospective future developments.
Enhanced Cluster Merging and Deep Learning Techniques for Entity Name Identification from Biomedical Corpus
For mining biomedical information identifying names is the prime task. Complex and uncertain naming styles of biomedical entities are the major setbacks here. Thus, state-of-the-art accuracy of biomedical name identification is reasonably inferior compared to general domain. This study includes machine learning and deep learning techniques to recognize names from biomedical corpus. In supervised classification, a classifier is built by finding required statistics from training corpus. Accordingly, performance of the system is primarily dependent on quantity and quality of training corpus. But manually preparing a large training dataset with enriched feature samples is laborious and time-taking. Therefore, various techniques were adopted in the literature to make effective use of raw corpora. We have incorporated a novel Cluster Merging technique and Attention Mechanism with BERT embedding for boosting machine learning and deep learning classifiers respectively. The suggested results outpour that profound techniques are competent and delineate signifying improvement over surviving methods.
Biometrics-Based Generation of Diffie-Hellman Key Exchange Parameters
When two parties need to securely communicate over an insecure channel, Diffie-Hellman is often employed as the key exchange algorithm. This paper presents two novel approaches to generating Diffie-Hellman parameters for key exchange based on user biometrics, namely their fingerprint data. Fingerprint templates are extracted as bit strings via a fingerprint scanner and later used as inputs. In one approach, the whole fingerprint template is utilized as a user’s private key. In the second approach, fingerprint data is scrambled into smaller chunks and rearranged into two strings that serve as the user’s private key and the basis for prime p. Both approaches were implemented and tested experimentally. After analysis, the second approach that uses scrambled fingerprint data shows better execution times and improved security and usability considerations.
Formalization and analysis of uml 2.0 interaction overview diagram using maude rewriting logic language
The visual modeling language UML embodies object-oriented design principles. It provides a standard way to visualize the design of a system. It exploits a rich set of well-defined graphical notations for creating abstract models. However, the power of UML is lessened through partially specified formal semantics. Indeed, UML notations are semi-formal and do not lead to fully formalized and executable semantics. Fortunately, UML diagrams are prone to early formalization. Formal methods are a valuable tool that can help overcome the UML constructs’ shortage of firm semantics. It is a powerful way to ascribe precise semantics to the graphical notations used in UML diagrams and models. Our work aims to support the semantics of the UML Interaction Overview Diagram. It introduces an approach to leveraging the strengths of the Maude Rewriting Logic language as a formal specification language. The proposal relies on a model-driven engineering approach. It aims to automate the UML Interaction Overview Diagram’s mapping to a Maude language specification. The Maude language and its linked tools, including the Maude Model Checker, are used to analyze and verify the resulting Maude specification. Finally, an application example shows the feasibility and benefits of the proposed approach.
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.
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.
A novel hybrid deep learning approach for 3d object detection and tracking in autonomous driving
Recently Object detection and tracking using fusion of LiDAR and RGB camera for the autonomous vehicle environment is a challenging task. The existing works initiates several object detection and tracking frameworks using Artificial Intelligence (AI) algorithms. However, they were limited with high false positives and computation time issues thus lacking the performance of autonomous driving environment. The existing issues are resolved by proposing Hybrid Deep Learning based Multi Object Detection and Tracking (HDL-MODT) using sensor fusion methods. The proposed work performs fusion of solid state LiDAR, Pseudo LiDAR, and RGB camera for improving detection and tracking quality. At first, the multi-stage preprocessing is done in which noise removal is performed using Adaptive Fuzzy Filter (A-Fuzzy). The pre-processed fused image is then provided for instance segmentation to reduce the classification and tracking complexity. For that, the proposed work adopts Lightweight General Adversarial Networks (LGAN). The segmented image is provided for object detection and tracking using HDL. For reducing the complexity, the proposed work utilized VGG-16 for feature extraction which forms the feature vectors. The features vectors are then provided for object detection using YOLOv4. Finally, the detected objects were tracked using Improved Unscented Kalman Filter (IUKF) and mapping the vehicles using time based mapping by considering their RFID, velocity, location, dimension and unique ID. The simulation of the proposed work is carried out using MATLAB R2020a simulation tool and performance of the proposed work is compared with several metrics that show that the proposed work outperforms than the existing works.