Discover computing最新文献

{"title":"Design and performance evaluation of a green LED OFDM LiFi system for an electromagnetic interference sensitive hospital network.","authors":"Ajay Sharma, Lalit Garg, Ahmad Atieh, Peter A Xuereb","doi":"10.1007/s10791-026-09906-0","DOIUrl":"10.1007/s10791-026-09906-0","url":null,"abstract":"<p><p>Light Fidelity (LiFi) is an alternative technology to Wireless Fidelity (WiFi) for secure, high-speed hospital communication. The main objective of this study is to design a Four-Quadrature Amplitude Modulation-Orthogonal Frequency Division Multiplexing (4QAM-OFDM) LiFi system that overcomes electromagnetic interference (EMI), ensures biological safety, guarantees secure medical data transmission, and delivers high-speed, low-latency connectivity for hospital networks. The core contribution is a holistic 4QAM-OFDM LiFi design that offers superior spectral efficiency, significantly reduced Bit Error Rate (BER), and compliance with healthcare safety standards compared to existing LiFi systems, as demonstrated by its simulation using OptiSystem 21 and MATLAB R2024b. Using a 500 nm Light-Emitting Diode (LED) compliant with photobiological safety standards safeguards biological safety, while utilizing 1024-subcarrier OFDM decreases ISI. The receiver's Positive-Intrinsic-Negative (PIN) photodetector converts optical signals to electrical form, while the quadrature demodulator minimizes phase distortion, achieving a BER of 4.25E-3 at 30 dBm-further reducible to E-9 with error correction for reliable hospital communication. This performance demonstrates the system's suitability for mission-critical applications such as AI-assisted diagnostics, robotic surgery, and real-time medical imaging. The proposed system maintained excellent tolerance to both multipath distortion and external EMI, resolving EMI-related device interference, improving energy efficiency through reduced power consumption, and enhancing security via optical confinement that prevents signal leakage beyond hospital rooms. This enables a practical and scalable pathway for replacing WiFi in hospital environments, ensuring uninterrupted, high-speed, and safe communication for both routine and life-critical healthcare applications. The system reduces power consumption, diminishes CO₂ emissions, and improves hospital energy efficiency by promoting sustainable and eco-friendly LiFi technology. This study confirms LiFi as a secure, high-performance WiFi alternative for hospitals, meeting healthcare standards.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"29 1","pages":"30"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12812083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.","authors":"James C L Chow","doi":"10.1007/s10791-026-10078-0","DOIUrl":"10.1007/s10791-026-10078-0","url":null,"abstract":"<p><p>Machine learning (ML) is transforming cancer imaging by enhancing diagnosis, automating image analysis, and improving treatment planning. This review explores key ML applications, including tumor detection, radiomics, multi-modal imaging, and therapy monitoring. We discuss fundamental ML techniques, deep learning architectures, and data preprocessing strategies essential for medical imaging. ML-driven approaches have improved tumor segmentation, feature extraction, and computer-aided diagnosis across various cancer types. In cancer therapy, artificial intelligence (AI) aids radiotherapy planning, treatment response prediction, and real-time image-guided interventions. However, challenges such as data scarcity, model bias, and regulatory hurdles limit clinical adoption. Emerging solutions include explainable AI, federated learning for data privacy, and quantum computing for advanced imaging. Addressing these challenges through interdisciplinary collaboration will accelerate AI integration into clinical practice, enhancing cancer diagnosis and treatment.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"29 1","pages":"186"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13035700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147597544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Common TF-IDF variants arise as key components in the test statistic of a penalized likelihood-ratio test for word burstiness.","authors":"Zeyad Ahmed, Paul Sheridan, Michael McIsaac, Aitazaz A Farooque","doi":"10.1007/s10791-026-10090-4","DOIUrl":"10.1007/s10791-026-10090-4","url":null,"abstract":"<p><p>TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores arise naturally from the test statistic of a penalized likelihood-ratio test setup capturing word burstiness (also known as word over-dispersion). In our framework, the alternative hypothesis captures word burstiness by modeling a collection of documents according to a family of beta-binomial distributions with a gamma penalty term on the precision parameter. In contrast, the null hypothesis assumes that words are binomially distributed in collection documents, a modeling approach that fails to account for word burstiness. We find that a term-weighting scheme given rise to by this test statistic performs comparably to TF-IDF on document classification tasks. This paper provides insights into TF-IDF from a statistical perspective and underscores the potential of hypothesis testing frameworks for advancing term-weighting scheme development.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"29 1","pages":"274"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic privacy and security in the era of artificial intelligence and quantum computing.","authors":"Richard Annan, Justin Noland, Kamaria Perkins, Xiaohong Yuan, Kaushik Roy, Letu Qingge","doi":"10.1007/s10791-025-09627-w","DOIUrl":"10.1007/s10791-025-09627-w","url":null,"abstract":"<p><p>The rapid advancements in sequencing technologies have greatly increased access to genomic data stored in public databases. This has raised significant privacy and security concerns. This review emphasizes the importance of protecting genomic data by analyzing vulnerabilities in current storage and sharing practices. It examines the risks genetic databases face from cyber-attacks and internal breaches, focusing especially on advanced AI-driven threats and quantum computing vulnerabilities. The review explores machine learning methods designed to secure data. It highlights algorithms that prioritize privacy while maintaining data confidentiality, such as differential privacy, federated learning, and synthetic data generation using Generative Adversarial Networks (GANs). Findings demonstrate progress in mitigating common privacy breaches like re-identification and inference attacks. However, persistent vulnerabilities remain, particularly to emerging threats such as model inversion and membership inference attacks. The review advocates an integrated approach combining robust legislative frameworks with advanced technology to address genomic privacy challenges. It calls for intensified research efforts to safeguard genomic information. In particular, there is an urgent need to adopt quantum-resistant cryptographic methods, including lattice-based encryption and blockchain-integrated security frameworks. The paper emphasizes the necessity for genomics researchers to prioritize data privacy and security. This ensures responsible handling of genomic information in research.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"28 1","pages":"108"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LSTM guided homomorphic encryption for threat-resistant IoT networks.","authors":"Sanjeev Kumar, Sukhvinder Singh Deora, Tajinder Kumar, Purushottam Sharma, Xiaochun Cheng, Vishal Garg","doi":"10.1007/s10791-025-09843-4","DOIUrl":"10.1007/s10791-025-09843-4","url":null,"abstract":"<p><p>The rapid growth of the Internet of Things (IoT) has led to revolutionary innovations in many fields; however, it has also resulted in significant security and privacy issues due to the resource limitations and distributed nature of IoT networks. Traditional cryptographic techniques or machine learning-based anomaly detection systems do not jointly provide data privacy and resilience to threats in real time. The existing methods, such as Homomorphic Encryption (HE), offer a high computation cost for performing encryption. Furthermore, Long Short-Term Memory (LSTM) networks can predict an anomaly profile instead of performing encryption. To address these shortcomings, this paper proposes NeuroCrypt. This new hybrid system combines Fully Homomorphic Encryption (FHE) with LSTM-based encrypted anomaly detection and supplements it with blockchain-based dynamic key management and multi-factor authentication. The architecture targets edge and fog computing settings using, among other techniques, ciphertext packing, model quantisation, and parallelised encrypted operations. The performance of the proposed framework has been evaluated on a real dataset. The results show that the accuracy in the proposed framework is 99.2% compared to existing techniques such as HE-based DNN, FL-based models, and LSTM IDS. Conclusively, NeuroCrypt provides a privacy-preserving, effective, and scalable solution to real-time threat abatement in IoT networks.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"28 1","pages":"301"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12689805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145746610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penalized GANs with latent perturbation for robust shilling attack generation in recommender systems.","authors":"Dina Nawara, Rasha Kashef","doi":"10.1007/s10791-025-09702-2","DOIUrl":"https://doi.org/10.1007/s10791-025-09702-2","url":null,"abstract":"<p><p>Shilling attacks pose a significant threat to the integrity and reliability of recommender systems by injecting fake user profiles to promote or demote targeted items. Existing generative approaches often suffer from unstable training dynamics and limited realism in the synthesized profiles. In this paper, we propose PGAN, a novel Penalized Generative Adversarial Network enhanced with latent space perturbations to generate high-quality, diverse, and undetectable shilling attack profiles. PGAN incorporates a gradient penalty to stabilize discriminator training and applies controlled noise perturbations in the generator's latent space to improve robustness and attack diversity. We evaluate PGAN on real-world datasets and demonstrate that it consistently outperforms traditional statistical attacks and baseline GAN-based models across multiple evaluation metrics, including Hit Ratio@K, Prediction Shift, and attack success rate. Experimental results also confirm the realism of the generated profiles through similarity analysis with genuine users. Our proposed model outperforms traditional and state-of-the-art methods, achieving HR@10 scores of 0.2051 and 0.2076 on the MovieLens and Amazon datasets, respectively.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"28 1","pages":"176"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12370797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144987218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the emotional accuracy of AI-generated facial expressions in neurotypical individuals.","authors":"Antonio Pagán, Katherine A Loveland, Ronald Acierno","doi":"10.1007/s10791-025-09630-1","DOIUrl":"10.1007/s10791-025-09630-1","url":null,"abstract":"<p><p>This study examines the ability of generative artificial intelligence to produce facial expressions representing basic emotions in a neutral context using black-and-white cartoon imagery. Mentalization, the capacity to recognize and interpret one's own and others' mental states, is critical for social interaction and emotional regulation. We explored the emotional validation of artificial intelligence (AI)-generated images by assessing the agreement between human interpretations of emotions and those generated by an AI model. Thirty-four participants evaluated images depicting six basic emotions: sadness, anger, happiness, surprise, fear, and disgust. Our findings revealed significant variability in human agreement, with higher concordance for sadness, anger, and happiness (87%, 73%, and 69%, respectively) and lower agreement for fear, surprise, and disgust (3%, 9%, and 14%, respectively). No significant gender differences were found in emotion recognition accuracy, although a positive correlation between age and accuracy indicated that older participants may possess greater emotional insight. These results suggest that while AI can effectively replicate certain emotional expressions, its capacity to convey more nuanced emotions remains limited. The study underscores the need for advanced training data that encompass a broader range of emotional expressions and cultural nuances to enhance the applicability of AI in mental health and interpersonal communication. Furthermore, this research is particularly relevant for improving AI's ability to generate clear and accurate emotional cues could advance its utility as a tool for social and emotional learning.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s10791-025-09630-1.</p>","PeriodicalId":520898,"journal":{"name":"Discover computing","volume":"28 1","pages":"116"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12175737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144328442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}