This paper provides a comprehensive analysis of recent developments in face recognition, tracking, identification, and person detection technologies, highlighting the benefits and drawbacks of the available techniques. To assess the state-of-art in these domains, we reviewed more than one hundred eminent journal articles focusing on current trends and research gaps in machine learning and deep learning methods. A systematic review using the PRISMA method helped us to generalize the search for the most relevant articles in this area. Based on our screening and evaluation procedures, we found and examined 142 relevant papers, evaluating their reporting compliance, sufficiency, and methodological quality. Our findings highlight essential methods of person detection, tracking and identification, and face recognition tasks, emphasizing current trends and illustrating a clear transition from classical to deep learning methods with existing datasets, divided by task and including statistics for each of them. As a result of this comprehensive review, we agree that the results demonstrate notable improvements. Still, there remain several key challenges like refining model robustness under varying environmental conditions, including diverse lighting and occlusion; adaptation to different camera angles; and ethical and legal issues related to privacy rights.

In this study, we implemented YOLO (You Only Look Once) for real-time object detection and evaluated its performance based on key metrics such as processing speed, frame rate, and object detection accuracy. Our approach emphasizes both the precision and efficiency of YOLO, focusing on its ability to detect objects in real-world scenarios while maintaining a low computational cost. To identify and count objects, the YOLO algorithm was applied to analyze three images. It divided each image into a grid, and each cell predicted bounding boxes and confidence scores for potential objects. Following the processing of these predictions using non-max suppression to remove duplicates, each image contained an accurate count of the items that were detected. The model achieved a processing time of 17.68 seconds, with an average of 0.25 seconds per frame, demonstrating the system's capability for rapid object detection in near real-time applications. On average, 1.32 objects were detected per frame, with a maximum of 1.67 objects in a single frame and a minimum of 1 object per frame, indicating consistent detection across the dataset. The standard deviation of objects per frame (0.113) shows a low variability in object detection rates, reflecting the robustness of the model in handling diverse input frames. The achieved frame rate of 4.2 FPS demonstrates the model's potential for real-time applications, particularly in environments where processing speed is critical. The scientific novelty of this work lies in demonstrating YOLO’s adaptability for efficient object detection while maintaining high detection rates and consistent performance across varying scenarios. This study contributes to the field by showcasing YOLO's applicability in real-time systems, where object detection speed and accuracy are paramount. Our findings provide a foundation for further optimization in high-performance, low-latency object detection tasks, as well as its scalability for more complex detection systems. The results underscore YOLO's potential in both academic and industrial settings.

Жоғары өнімді биологиялық деректерді генерациялау жоғары өнімді есептеу (hpc) жүйелері мен суперкомпьютерлерде автоматтандырылған биоинформатика құбырларын енгізуге түрткі болды. prisma нұсқауларына сәйкес құрастырылған бұл жүйелі шолу 2018 және 2025 жылдар аралығында жарияланған 101 зерттеуді қорытындылайды, онда геномика, транскриптомика, протеомика және метагеномика салаларында hpc орталарында орналастырылған жұмыс процесін басқару жүйелері (wfms) зерттеледі. біз nextflow, snakemake, wdl және cwl сияқты қалыптасқан платформаларды талдап, енгізудегі қиындықтар мен жаңа шешімдерді құжаттадық. негізгі қиындықтарға жаппай параллелизмге байланысты жоспарлағыштың шамадан тыс жүктелуі, ортақ файлдық жүйелердегі енгізу/шығару кедергілері, ресурстардың гетерогенді таралуы және әртүрлі есептеу орталарындағы қайталанымдылық жатады. docker және singularity арқылы контейнерлеу портативтілік пен қайталанымдылықты қамтамасыз етудің басым шешімі ретінде пайда болды. nf-core сияқты қауымдастық бастамалары ең жақсы тәжірибелерге негізделген кураторлық құбырларды ұсыну арқылы осы технологияларды енгізуді жеделдетті. заманауи шешімдерге hpc-қа бейімделген жоспарлау стратегиялары, гибридті бұлт және hpc архитектуралары, сондай-ақ машиналық оқытуға негізделген талдау үшін gpu интеграциясы кіреді. күрделі көп сатылы талдауларды автоматтандырудағы айтарлықтай жетістіктерге қарамастан, жұмыс процесі жүйелері мен hpc инфрақұрылымының үздіксіз бірлескен эволюциясы эксзамасштабты деректер көлемдерін өңдеу және масштабта толық қайталанатын есептеу биологиясына қол жеткізу үшін маңызды болып қала береді.

Traditional emotion recognition systems often rely on generalized person-centered models that do not consider the variability of individual emotion expression. This paper explores individual differences in emotion expression and facial expressions for recognizing mental disorders based on video streaming. Using machine learning techniques and deep learning algorithms, we aim to create an algorithm for emotion recognition using a personalized approach. The paper discusses the data collection methods, the condition and the impact of personalization on recognition accuracy. Experimental results demonstrate the advantages of automated personalized models over traditional models, highlighting their potential in the field of affective computing. The study also addresses ethical implications, advocating for bias-mitigated training through cross-cultural datasets and user-controlled calibration. With help of real-time edge computing, our system enables scalable, privacy-preserving mental health monitoring, underscoring the transformative potential of adaptive affective computing and remote diagnostics.

Predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) requires models that represent both regional brain abnormalities and their evolution across repeated examinations. We developed a longitudinal graph neural network that integrates structural magnetic resonance imaging, FDG-PET, regional imaging biomarkers, and clinical covariates across irregular follow-up visits. The study included 614 participants with baseline MCI from the Alzheimer’s Disease Neuroimaging Initiative: 218 converters to AD within five years and 396 non-converters, with 2438 eligible longitudinal visits. Each visit was represented as an 82-node brain graph based on the Desikan–Killiany atlas. Node features combined a 128-dimensional multimodal convolutional embedding with four regional biomarkers. Graph-attention layers modelled spatial dependencies, a node-wise gated recurrent unit modelled longitudinal dependencies, and masked temporal self-attention aggregated variable-length visit sequences. Participants were divided at the subject level into development and held-out test sets, and hyperparameters were selected by five-fold cross-validation within the development set. On the held-out test set of 123 participants, the model reached an area under the receiver operating characteristic curve of 0.859 (95% CI 0.795–0.915), balanced accuracy of 0.805 (95% CI 0.736–0.862), sensitivity of 0.781, and specificity of 0.832. The AUC was numerically higher than that of the strongest baseline, a CNN–GRU sequence model, which reached 0.832 (95% CI 0.758–0.894); the paired AUC difference was 0.027 (95% CI 0.009–0.098), the unadjusted DeLong p-value was 0.026, and the Holm-adjusted p-value was 0.052, which was not significant at the conventional 0.05 threshold after correction for multiple comparisons. In ablation experiments, removing temporal modelling reduced the AUC to 0.818, and removing the spatial graph structure reduced it to 0.808, the two largest reductions observed. Integrated-gradient analysis placed the highest importance on hippocampal and entorhinal regions. Combining graph-based spatial modelling with recurrent longitudinal reasoning was associated with higher discrimination than sequence modelling alone, though this difference was not statistically significant after correction for multiple comparisons. Validation was restricted to a single research cohort (ADNI), and no independent external dataset was used; prospective external validation on an independent cohort is required before the model can be considered for clinical use Because FDG-PET was unavailable for 19.3% of visits, we report the headline result separately from a sensitivity analysis restricted to participants with complete FDG-PET at every visit (development set cross-validated AUC 0.891 vs. 0.874 for the full cohort with masked missing FDG-PET); multimodal performance should be read as cohort-dependent rather than as a single unconditional figure.