Parallel Data Research Articles

Optimization of Parallel Data Transfers in Multi-Tiered Persistent Storage

A logical model of multi-tiered persistent storage provides a view of data where all available storage resources are distributed over a number of levels depending on the data transfer parameters and capacities. The efficient parallelization of data transfers in multi-tiered persistent storage is a significant challenge for a pipelined data processing model. This work examines a category of database applications implemented as sequences of operations that transfer data between the levels of multi-tiered persistent storage. The concept of EPN: Extended Petri Nets represents how database applications can be processed in parallel. A proposed transformation involves converting EPN into sequences of parallel data transfers. Additionally, a method is demonstrated for partitioning these sequences of data transfers, with the goal of reducing the total number of conflicts when data transfers occur between the levels of multi-tiered persistent storage. The paper proposes new rule-based algorithms for scheduling parallel data transfers that minimize total data transfer time. The objectives of the new algorithms are to evenly distribute the workload among the data transfer processes and reduce their idle time. Several experiments have confirmed the effectiveness of the new algorithms in generating parallel data transfer plans.

DISTRIBUTED HIGH-PERFORMANCE COMPUTING METHODS FOR ACCELERATING DEEP LEARNING TRAINING

This paper comprehensively analyzes distributed high-performance computing methods for accelerating deep learning training. We explore the evolution of distributed computing architectures, including data parallelism, model parallelism, and pipeline parallelism, and their hybrid implementations. The study delves into optimization techniques crucial for large-scale training, such as distributed optimization algorithms, gradient compression, and adaptive learning rate methods. We investigate communication-efficient algorithms, including Ring All Reduce variants and decentralized training approaches, which address the scalability challenges in distributed systems. The research examines hardware acceleration and specialized systems, focusing on GPU clusters, custom AI accelerators, high-performance interconnects, and distributed storage systems optimized for deep learning workloads. Finally, we discuss this field's challenges and future directions, including scalability-efficiency trade-offs, fault tolerance, energy efficiency in large-scale training, and emerging trends like federated learning and neuromorphic computing. Our findings highlight the synergy between advanced algorithms, specialized hardware, and optimized system designs in pushing the boundaries of large-scale deep learning, paving the way for future breakthroughs in artificial intelligence.

Inhibition of the long non-coding RNA MALAT1 downregulates MAP2K1 to suppress the progression of hypopharyngeal squamous cell carcinoma.

This study aimed to explore the role of lncRNA MALAT1 (Long non-coding RNAs metastasis-associated lung adenocarcinoma transcript), and its underlying mechanisms in Hypopharyngeal Squamous Cell Carcinoma (HSCC). Quantitative real-time PCR (qRT-PCR) was employed to measure lncRNA MALAT1 expression in HSCC and adjacent non-cancerous tissues, along with the expression of the downstream target mitogen-activated protein kinase kinase 1 (MAP2K1). The independent prognostic significance of lncRNA MALAT1 was assessed using Cox regression analysis. Potential downstream targets of MALAT1 were identified through PRM analysis and validated using the TCGA-HNSC database, Western blotting, and immunohistochemistry. CCK-8, flow cytometry, and Transwell assays were conducted to assess the effects of the lncRNA MALAT1/MAP2K1 axis on FaDu cells. Additionally, a nude mouse xenograft model was used to confirm the role of lncRNA MALAT1/MAP2K1 in tumor growth. LncRNA MALAT1 was significantly upregulated in HSCC tissues and closely associated with poor prognosis. Bioinformatics analysis, including parallel reaction monitoring (PRM) screening and TCGA-HNSC data, identified FERMT2, CSNK2A2, and MAP2K1 as potential downstream targets of MALAT1. Validation through qPCR, Western blotting, and immunohistochemistry confirmed MAP2K1 as a downstream target. In vitro and in vivo experiments demonstrated that inhibiting lncRNA MALAT1 suppressed cell proliferation, migration, and epithelial-to-mesenchymal transition (EMT) by downregulating MAP2K1 expression. Additionally, it induced apoptosis, affected the cell cycle, and inhibited tumor growth. Our study uniquely demonstrates that targeting MALAT1 significantly impedes HSCC progression by downregulating its novel downstream target, MAP2K1, offering new insights into potential therapeutic strategies.

Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

Parallel power loads anomalies are processed by a fast-density peak clustering technique that capitalizes on the hybrid strengths of Canopy and K-means algorithms all within Apache Mahout’s distributed machine-learning environment. The study taps into Apache Hadoop’s robust tools for data storage and processing, including HDFS and MapReduce, to effectively manage and analyze big data challenges. The preprocessing phase utilizes Canopy clustering to expedite the initial partitioning of data points, which are subsequently refined by K-means to enhance clustering performance. Experimental results confirm that incorporating the Canopy as an initial step markedly reduces the computational effort to process the vast quantity of parallel power load abnormalities. The Canopy clustering approach, enabled by distributed machine learning through Apache Mahout, is utilized as a preprocessing step within the K-means clustering technique. The hybrid algorithm was implemented to minimise the length of time needed to address the massive scale of the detected parallel power load abnormalities. Data vectors are generated based on the time needed, sequential and parallel candidate feature data are obtained, and the data rate is combined. After classifying the time set using the canopy with the K-means algorithm and the vector representation weighted by factors, the clustering impact is assessed using purity, precision, recall, and F value. The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. Additionally, tests demonstrate that combining canopy and the K-means algorithm to analyze data performs consistently and dependably on the Hadoop platform and has a clustering result that offers a scalable and effective solution for power system monitoring.

Real-time tracking of the Bragg peak during proton therapy via 3D protoacoustic Imaging in a clinical scenario

Proton radiotherapy favored over X-ray photon therapy due to its reduced radiation exposure to surrounding healthy tissues, is highly dependent on the accurate positioning of the Bragg peak. Existing methods like PET and prompt gamma imaging to localize Bragg peak face challenges of low precision and high complexity. Here we introduce a 3D protoacoustic imaging with a 2D matrix array of 256 ultrasound transducers compatible with 256 parallel data acquisition channels provides real-time imaging capability (up to 75 frames per second with 10 averages), achieving high precision (5 mm/5% Gamma index shows accuracy better than 95.73%) at depths of tens of centimeters. We have successfully implemented this method in liver treatment with 5 pencil beam scanning and in prostate cancer treatment on a human torso phantom using a clinical proton machine. This demonstrates its capability to accurately identify the Bragg peak in practical clinical scenarios. It paves the way for adaptive radiotherapy with real-time feedback, potentially revolutionizing radiotherapy by enabling closed-loop treatment for improved patient outcomes.

Assessing speaking proficiency of English teachers through self-assessment test: East Wollega zone in focus

This study addresses the significant role of English speaking proficiency in the teaching-learning process, particularly considering the questionable speaking abilities of English teachers. The research aims to assess the speaking proficiency of English teachers in the East Wollega Zone. Employing a convergent parallel mixed design, blending quantitative and qualitative data methods, self-assessment tests, and classroom observations were utilized for data collection, and 101 participants for self-assessment and 15 respondents for classroom observations were selected using simple random sampling method. Findings indicate a prevalent trend of low speaking proficiency among English teachers, supported by both self-assessment data (mean= 2.3, standard deviation= 0.62) and classroom observation records. While gender did not significantly influence self-assessment scores, significant differences were noted among education qualification levels and teaching experience groups. Multiple regression analysis revealed a moderate association (R2= 44.1%) between independent and dependent variables. The study concludes with implications underscoring the urgent need to enhance English teachers’ speaking competency, particularly in first-cycle primary schools in the East Wollega Zone, emphasizing the potential to elevate the quality of the teaching-learning process and improve students' language acquisition and overall educational outcomes.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Impacts of Big Data Analytics for Agriculture tools and techniques

The present paper deals with “Impacts of Big Data Analytics for Agriculture tools and techniques.” A random sample 30 Various software platforms are developed to give information to farmers about new tools and techniques related to agriculture: (MySmartFarm, Awhere weather, Phenone, Farmlog, Datafloq, Farmeron) has been used by secondary source from Agriculture depart in the state of Bihar. The traditional tools and techniques (Big data) analysis massive amount of data. To store and analyze this type of data parallel computing and analyze paradigm is required. Big data analytic is used to weather changes and its impacts of agriculture in Bihar. From the big data analytic Agriculture framework is developed that identify disease based on symptoms similarity and recommend a solution based on high similarity and achieve their tools has been used. Then cleansing of data is done that is important information is extracted from unstructured redundant data and were normalization is done that is features are extracted from cleaned data. The data was used to analyze the agricultural tools and technique. It finds out disease name based on weather changes and its impacts of agriculture has been taken on past data. Result has been shows that data analytic in weather changes past 2018-2023 years that was.

Detector-embedded reconstruction of complex primitives using FPGAs

The slowdown of Moore’s law and the growing requirements of future HEP experiments with ever-increasing data rates pose important computational challenges for data reconstruction and trigger systems, encouraging the exploration of new computing methodologies. In this work we discuss a FPGA-based tracking system, relying on a massively parallel pattern recognition approach, inspired by the processing of visual images by the natural brain (“retina architecture”). This method allows a large efficiency of utilisation of the hardware, low power consumption and very low latencies. Based on this approach, a device has been designed within the LHCb Upgrade-II project, with the goal of performing track reconstruction in the forward acceptance region in real-time during the upcoming Run 4 of the LHC. This innovative device will perform track reconstruction before the event-building, in a short enough time to provide pre-reconstructed tracks (“primitives”) transparently to the processor farm, as if they had been generated directly by the detector. This allows significant savings in higher-level computing resources, enabling handling higher luminosities than otherwise possible. The feasibility of the project is backed up by the results of tests performed on a realistic hardware prototype, that has been opportunistically processing actual LHCb data in parallel with the regular DAQ in the LHC Run 3.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Discovering vaccines: the trial tale.

This paper reviews the importance of vaccine trials to public health. Vaccines are developed to build immunity to disease, which have helped to completely eliminate smallpox, and reduced incidence of polio and measles. A variety of breakthroughs made many years ago, such as the smallpox vaccine by Edward Jenner and the discoveries by Louis Pasteur, also set the stage for modern international immunization programs. A few events, such as the licensing of the polio vaccine and the passage of the Vaccination Assistance Act helped to improve the study of vaccines. In particular, vaccine trials may be RCTs, cluster trials, or cohort studies. The sample sizes will depend on the objectives, which would include the primary and secondary endpoints. The population under study and the geographical location also affect the trial design. Preclinical evaluation is usually the starting point of vaccine trials, where the safety and efficacy are researched on animal models or cell cultures. Animal models are selected based on their similarity to the target disease. Safety is checked in Phase I, efficacy in Phase II, and both in Phase III. Phase IV is a post-marketing surveillance of the vaccine's safety in real life. Regulatory bodies play a very vital role in ensuring that vaccines adhere to a very high standard of safety and efficacy, such as the FDA, as required. Ethical considerations, such as informed consent and the rights of participants, are innate and are implemented and enforced through laws, regulations, and ethical committees. Vaccine studies vary from the drug studies as it is focused on preventing illness in healthy patients as opposed to cure of diseases in drug trials. The dramatic development in vaccine research was driven by recent pandemics, with parallel processing and data collection in real time. Clinical trials of the vaccine are a foundation stone of public health in the reduction of sickness, offering immunity to diseases, and continuing the fight against infectious diseases. The present review is aimed at describing vaccine trials and their important aspects.

Clinical and biobehavioral phenotypic assessments and data harmonization for the RE-JOIN research consortium: Recommendations for common data element selection

BackgroundThe Restoring Joint Health and Function to Reduce Pain (RE-JOIN) Consortium is part of the Helping to End Addiction Long-term® (HEAL) Initiative. HEAL is an ambitious, NIH-wide initiative to speed scientific solutions to stem the national opioid public health crisis. The RE-JOIN consortium’s over-arching goal is to define how chronic joint pain-mediating neurons innervate different articular and peri-articular tissues, with a focus on the knee and temporomandibular joints (TMJ) across species employing the latest neuroscience approaches. The aim of this manuscript is to elucidate the human data gathered by the RE-JOIN consortium, as well as to expound upon its underlying rationale and the methodologies and protocols for harmonization and standardization that have been instituted by the RE-JOIN Consortium. MethodsThe consortium-wide human models working subgroup established the RE-JOIN minimal harmonized data elements that will be collected across all human studies and set the stage to develop parallel pre-clinical data collection standards. Data harmonization considerations included requirements from the HEAL program and recommendations from the consortium’s researchers and experts on informatics, knowledge management, and data curation. ResultsMultidisciplinary experts − including preclinical and clinical researchers, with both clinician-scientists- developed the RE-JOIN’s Minimal Human Data Standard with required domains and outcome measures to be collected across projects and institutions. The RE-JOIN minimal data standard will include HEAL Common Data Elements (CDEs) (e.g., standardized demographics, general pain, psychosocial and functional measures), and RE-JOIN common data elements (R-CDE) (i.e., both general and joint-specific standardized and clinically important self-reported pain and function measures, as well as pressure pain thresholds part of quantitative sensory testing). In addition, discretionary, site-specific measures will be collected by individual institutions (e.g., expanded quantitative sensory testing and gait biomechanical assessments), specific to the knee or TMJ. Research teams will submit datasets of standardized metadata to the RE-JOIN Data Coordinating Center (DCG) via a secure cloud-based central data repository and computing infrastructure for researchers to share and conduct analyses on data collected by or acquired for RE-JOIN. RE-JOIN datasets will have protected health information (PHI) removed and be publicly available on the SPARC portal and accessible through the HEAL Data Ecosystem. ConclusionData Harmonization efforts provide the multidisciplinary consortium with an opportunity to effectively collaborate across decentralized research teams, and data standardization sets the framework for efficient future analyses of RE-JOIN data collected by the consortium. The harmonized phenotypic information obtained will significantly enhance our understanding of the neurobiology of the pain-pathology relationships in humans, providing valuable insights for comparison with pre-clinical models.

GPU accelerated Staggered Update Procedure (SUP)

The advancement in programmable capability of graphics hardware has paved new opportunities in the domain of high performance computing (HPC). The computational fluid dynamics (CFD) community, being a significant user of HPC, has started exploiting the inherent data parallelism in the numerical solvers to be able to make efficient use of these many-core, high throughput accelerator based processors. In the present work, we examine the process of accelerating our CPU based Staggered Update Procedure (SUP) solver, i.e., a higher order accurate cell-centred finite volume solver by off-loading the computationally most expensive region of the code pertaining to the explicit residual computation. We have adopted OpenACC, a directive based programming model to expose parallelism in the code. The framework evolved for GPU porting in the context of SUP is also of value to those intending to port their CFD solvers based on classical finite volume methodology. The performance analysis is conducted using scalar convection–diffusion equations in both two- and three-dimensions. The findings demonstrate a speedup factor of 9 (in case of 2D) and 28 (in case of 3D) when considering the explicit residual alone, achieved with a single NVIDIA Tesla V100 GPU card. In addition, we could establish superior algorithmic scalability by the way of recovering near perfect serial performance, on the heterogeneous CPU+GPU architecture. Further, overall code acceleration can be achieved by porting other parts of the solver on GPU.

A Single-Pixel Event Photoactive Device for Real-Time, In-Sensor Spatiotemporal Optical Information Processing.

The increasing demand for energy-efficient, sophisticated optical sensing technologies in various applications, from machine vision to optical communication, highlights the necessity for innovations in spatiotemporal information sensing and processing at a nearly single-pixel scale. Traditional methods, including multi-pixel photodetector arrays and event-based camera systems, often fail to provide rapid, real-time detection and processing of dynamic events within the sensor. This shortcoming is particularly notable in handling high-dimensional spatiotemporal data, where the dependency on sequential data input and external processing tools leads to latency, reduced throughput, and heightened energy consumption, thereby impeding real-time parallel data processing capabilities. Here, a carrier-selective, single-pixel, position-sensitive planar photoactive device that integrates spatiotemporal event sensing with inherent short-term memory capabilities is introduced. The proof-of-concept single-pixel event photoactive device enables in-sensor spatiotemporal parallel optical information processing, efficiently managing multibit (>4 bit) data simultaneously and facilitating ultrafast (≈0.4µs) recognition of input patterns with low energy consumption (25 femtojoules). Additionally, by adjusting the operating speed from continuous to pulsed light illumination, the sensor array can detect trajectories and absolute position of events, offering in-sensor optical flow detection. This single-pixel event photodetector marks significant advancement toward developing compact, energy-efficient, ultrafast sensors suitable for a wide range of in sensor-based photonic applications.

A Review of Artificial Intelligence and Machine Learning in Product Life Cycle Management.

The pursuit of harnessing data for knowledge creation has been an enduring quest, with the advent of machine learning and artificial intelligence (AI) marking significant milestones in this journey. Machine Learning (ML), a subset of AI, emerged as the practice of employing mathematical models to enable computers to learn and improve autonomously based on their experiences. In the pharmaceutical and biopharmaceutical sectors, a significant portion of manufacturing data remains untapped or insufficient for practical use. Recognizing the potential advantages of leveraging available data for process design and optimization, manufacturers face the daunting challenge of data utilization. Diverse proprietary data formats and parallel data generation systems compound the complexity. The transition to Pharma 4.0 necessitates a paradigm shift in data capture for manufacturing and process operations. This paper highlights the pivotal role of artificial intelligence in converting process data into actionable knowledge to support critical functions throughout the whole process life cycle. Furthermore, it underscores the importance of maintaining compliance with data integrity guidelines, as mandated by regulatory bodies globally. Embracing AI-driven transformations is a crucial step toward shaping the future of the pharmaceutical industry, ensuring its competitiveness and resilience in an evolving landscape.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

Research on image compression technology based on improved SPIHT compression algorithm for power grid data

Abstract At present, the amount of information on power grid operation and maintenance monitoring image data is increasing, and the requirements for data compression are higher and higher. Based on the improved SPIHT image compression algorithm, this study presents the research of power grid data compression. First, the basic theory of image compression, and the principle of one-dimensional wavelet transform and two-dimensional wavelet transform are introduced. The development process, characteristics, and advantages of image coding are discussed. Then, the shortcomings of the SPIHT algorithm are analyzed, and the SPIHT coding is improved by parallel computation. The parallel wavelet transform algorithm based on the block idea and the parallel SPIHT coding algorithm based on the code tree are proposed in the data parallelism of the compression algorithm. At the same time, the data dependence between tasks in the process of SPIHT image compression coding is analyzed, and the task parallelism in the compression algorithm is realized by using the relative independence of tasks in different threshold coding. Finally, the application and simulation analysis of power grid data based on the SPIHT compression algorithm, the construction of power grid data model simulation, and the composition of two-dimensional power grid data images are carried out. Secondly, the obtained 2D power grid data image is compressed by the SPIHT algorithm and improved SPIHT algorithm, respectively, and the compression effect of the two algorithms on the power grid data image is compared. When the bit rate is 0.5, the compression effect of the improved SPIHT algorithm is 13.6506. When the bit rate is 1, the compression effect of the improved SPIHT algorithm is 18.9287. The results show that the improved SPIHT algorithm can compress the grid data to obtain better grid image quality.

Pulse program for improving learning accuracy and reducing programming energy consumption of ferroelectric synaptic transistor

Neuromorphic computing is a next‐generation computing technology featured by parallel data processing and adaptive learning. Two significant factors that improve learning accuracy are the ‘dynamic range’ and ‘linearity’ of the weight update. In a ferroelectric synaptic transistor, the weight update can be modulated by adjusting the applied voltage. The voltage pulse train should be carefully optimized to improve the learning accuracy and reduce programming energy consumption. In this study, we investigated the learning accuracy of neuromorphic computing based on the characteristics of synaptic devices and the program energy consumption according to pulse programs. We demonstrated changes in the analog conductance characteristics of ferroelectric thin‐film transistors by varying the pulse program for synaptic plasticity, discussed the characteristics for improving learning accuracy, and compared the programming energy consumption according to the pulse programs. We proposed a logarithmic‐incremental‐step pulse program that reduces programming energy consumption and improves learning accuracy.

Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce

Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Spatial Clustering of Applications with Noise (I-DBSCAN) is proposed to cluster EHR data to get labels based on the symptoms of patients and map reduction is utilized to process the clustered data in parallel for quick recommendations. Findings: The accuracy of the SVM with the PSO model is 99.20%. The performance of I-DBSCAN is also compared with K-Means and regular DBSCAN using the Silhouette Score, Davies Bouldin Score, and the Calinski Harabasz Score. Also, I-DBSCAN was found to give a more accurate score. Novelty: The extensive volume of diabetes-related information stored in electronic health records (EHRs) through continuous monitoring devices poses a growing difficulty for healthcare professionals to effectively navigate and deliver patient-centered care. Machine Learning techniques like classification and recommendations can be utilized to facilitate early disease diagnosis and recommend appropriate medications. Keywords: Electronic health records (EHRs), Collaborative Filtering (CF), Recommendations, Improved Density Based Spatial Clustering of Applications with Noise (IDBSCAN), SVM classifier