2025-12-27
Shatakshi Kokate, Urmila Shrawankar
The increased use of IoT devices in various domains generates abundant data traffic. Securing this data during transfer and storage is essential. Blockchain is now a trending technology to provide security to the data. However, it is observed that blockchain performs poorly while managing large volume data. To mitigate this issue, an advanced Optchain method to reduce the data size before submitting it to the blockchain network is introduced in this paper. This method first classifies data as relevant and irrelevant and then applies compression to the appropriate data to reduce size. This reduces storage requirements, improves processing efficiency, ensures faster transactions, and lowers cost. The proposed method is tested for healthcare IoT data. Compared to current systems, the evaluation findings of the suggested solution show a notable reduction in data size. As data is reduced, it is easily managed by the blockchain network. Thus, the proposed Optchain method provides better security to the IoT-generated data.
2025-10-17
Sunil Upadhyay, Hemant Kumar Soni
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.
2025-10-17
Atul Kumar Uttam, Rohit Agrawal, Anand Singh Jalal
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.
2025-10-17
Kheirolah Rahsepar Fard, Ali Sarabadani, Hamid Dalvand
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.
2025-08-14
Rosa Petrini, Lucio Anderlini, Matteo Barbetti, Giulio Bianchini, Diego Ciangottini, Stefano Dal Pra, Diego Michelotto, Daniele Spiga
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.
2025-07-28
Wojciech Krzemien, Konrad Klimaszewski, Lech Raczynski
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.
2025-07-28
Luis Eduardo Suelves
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.
2025-07-28
Piotr Kalaczyński
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.
2025-07-28
Lech Raczyński, Wojciech Krzemień, Konrad Klimaszewski
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.