Processing Applications Research Articles

Emerging Trends in Real-time Recommendation Systems: A Deep Dive into Multi-behavior Streaming Processing and Recommendation for E-commerce Platforms

This paper aims to assess the effectiveness of recommendation systems, focusing on multi-behavior streaming processing to enhance accuracy. It studies recommendation system performance using AI and data science, focusing on user behaviour, algorithm refinement, and satisfaction. E-commerce, streaming, and social media datasets with 93 anonymous participants were employed in experiments. These files from 93 anonymized users show typical internet use. Data anonymization and strict data management ensured anonymity. These databases track clicks, views, purchases, and ratings for empirical research and model evaluation. Collaborative filtering, matrix factorization, and TensorFlow/Keras train and assess application-specific recommendation models. Multi-behavior streaming processing and recommendation accounts assess each method's pros and downsides, system correctness, efficacy, and user involvement. The outcome compares domain-wide recommendation system precision, recall, NDCG, and conversion rate. Multi-behavior streaming processing adapts to user preferences and interactions to improve model accuracy and adaptability. Multi-behavior streaming processing improved model accuracy and flexibility by reacting to user inputs and choices. With error margins for all significant metrics, statistical significance was confirmed. The findings suggest that recommendation systems with real-time adaptation and multi-behavior streaming processing can improve user satisfaction and engagement in the changing digital landscape. It encourages algorithm advancement, model interpretation, user-centric evaluation, and ethics to improve information retrieval and personalisation. The study concluded that algorithm refinement, transparent model interpretation, user-centric evaluation, and ethical problems including data protection and bias mitigation increase information retrieval and personalisation. For durable and flexible recommendation systems, research should improve multi-behavior processing and sectoral applications.

Design of Power Optimised Truncated Approximate Booth Multiplier

Abstract: In digital signal processing and arithmetic circuits, Booth multipliers are widely used for efficient multiplication of signed numbers. However, conventional Booth multipliers consume significant power due to their full precision operation. This paper explores efficient approximation techniques for Booth multipliers enhanced with flip-flop clock gating to reduce power consumption. Booth multipliers traditionally consume significant power due to their full precision operations. In this approach it mainly focuses on truncating less significant bits in the Booth encoding to lower computational demands without compromising performance. Additionally, flip-flop clock gating dynamically disables clock signals to idle flip-flops during non-active computation phases, further reducing power dissipation. Simulation results demonstrate substantial power savings compared to conventional Booth multipliers while maintaining acceptable accuracy for digital signal processing applications. These findings contribute to advancing energy-efficient arithmetic units, particularly beneficial for battery-powered devices and embedded systems where power efficiency is crucial

Variable gauge length: Processing theory and applications to distributed acoustic sensing

AbstractMost distributed acoustic sensing systems will only process acoustic data with a fixed gauge length for the entire well depth. However, it has been shown that the gauge length is a critical parameter to improve the signal‐to‐noise ratio when used as a function of certain geophysical parameters, such as the apparent velocity. It can also be responsible for significant distortions if tuned incorrectly. In this paper, we first aim to reintroduce the concept of gauge length and derive a robust method to optimize its value based on the geophysical parameters whilst ensuring no distortion to the original signal. We then present a novel method of processing the distributed acoustic sensing data using the concept of a variable gauge length. We finish by showing applications of these techniques on synthetic vertical seismic profiling data and some of the results obtained on actual field datasets.

Alternating Projection Method for Intersection of Convex Sets, Multi-Agent Consensus Algorithms and Averaging Inequalities

The history of the alternating projection method for finding a common point of several convex sets in Euclidean space goes back to the well-known Kaczmarz algorithm for solving systems of linear equations, which was devised in the 1930s and later found wide applications in image processing and computed tomography. An important role in the study of this method was played by I.I. Eremin’s, L.M. Bregman’s, and B.T. Polyak’s works, which appeared nearly simultaneously and contained general results concerning the convergence of alternating projections to a point in the intersection of sets, assuming that this intersection is nonempty. In this paper, we consider a modification of the convex set intersection problem that is related to the theory of multi-agent systems and is called the constrained consensus problem. Each convex set in this problem is associated with a certain agent and, generally speaking, is inaccessible to the other agents. A group of agents is interested in finding a common point of these sets, that is, a point satisfying all the constraints. Distributed analogues of the alternating projection method proposed for solving this problem lead to a rather complicated nonlinear system of equations, the convergence of which is usually proved using special Lyapunov functions. A brief survey of these methods is given, and their relation to the theorem ensuring consensus in a system of averaging inequalities recently proved by the first author is shown (this theorem develops convergence results for the usual method of iterative averaging as applied to the consensus problem).

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

Exploring the applications of natural language processing and language models for production, planning, and control activities of SMEs in industry 4.0: a systematic literature review

Application of CNN Classic Model in Modern Image Processing

As a deep learning model, convolutional neural network (CNN) has been widely used in the field of modern image processing and has shown excellent performance. From LeNet to AlexNet, to classic models such as VGGNet and ResNet, CNN has achieved remarkable success in tasks such as image classification, object detection and segmentation through multi-level feature extraction and automatic learning capabilities. This paper first explores the basic structure and working principle of CNN, analyzes the advantages and limitations of classic models, and reviews its specific applications in image processing. By introducing the optimization strategies of various models, it further explores the improvement path of CNN in the field of image processing, including model compression, lightweight design and the introduction of new network structures. In short, the continuous optimization of CNN has enabled it to show powerful performance in multiple complex tasks, promoted the rapid development of image processing technology, and provided strong support for applications in more fields.

Advanced multi-nozzle electrohydrodynamic printing: mechanism, processing, and diverse applications at micro/nano-scale

Abstract Electrohydrodynamic (EHD) jet printing represents a novel micro/nano-scale additive manufacturing process that utilises a high-voltage induced electric field between the nozzle and the substrate to print micro/nanoscale structures. EHD printing is particularly advantageous for the fabrication on flexible or non-flat substrates and of large aspect ratio micro/nanostructures and composite multi-material structures. Despite this, EHD printing has yet to be fully industrialised due to its low throughput, which is primarily caused by the limitations of serial additive printing technology. The parallel multi-nozzle array-based process has become the most promising option for EHD printing to achieve large-scale printing by increasing the number of nozzles to realise multichannel parallel printing. This paper reviews the recent development of multi-nozzle EHD printing technology, analyses jet motion with multi-nozzle, explains the origins of the electric field crosstalk effect under multi-nozzle and discusses several widely used methods for overcoming it. This work also summarises the impact of different process parameters on multi-nozzle EHD printing and describes the current manufacturing process using multi-nozzle as well as the method by which they can be realised independently. In addition, it presents an additional significant utilisation of multi-nozzle printing aside from enhancing single-nozzle production efficiency, which is the production of composite phase change materials through multi-nozzle. Finally, the future direction of multi-nozzle EHD printing development is discussed and envisioned.

A Survey on Design Space Exploration Approaches for Approximate Computing Systems

Approximate Computing (AxC) has emerged as a promising paradigm to enhance performance and energy efficiency by allowing a controlled trade-off between accuracy and resource consumption. It is extensively adopted across various abstraction levels, from software to architecture and circuit levels, employing diverse methodologies. The primary objective of AxC is to reduce energy consumption for executing error-resilient applications, accepting controlled and inherently acceptable output quality degradation. However, harnessing AxC poses several challenges, including identifying segments within a design amenable to approximation and selecting suitable AxC techniques to fulfill accuracy and performance criteria. This survey provides a comprehensive review of recent methodologies proposed for performing Design Space Exploration (DSE) to find the most suitable AxC techniques, focusing on both hardware and software implementations. DSE is a crucial design process where system designs are modeled, evaluated, and optimized for various extra-functional system behaviors such as performance, power consumption, energy efficiency, and accuracy. A systematic literature review was conducted to identify papers that ascribe their DSE algorithms, excluding those relying on exhaustive search methods. This survey aims to detail the state-of-the-art DSE methodologies that efficiently select AxC techniques, offering insights into their applicability across different hardware platforms and use-case domains. For this purpose, papers were categorized based on the type of search algorithm used, with Machine Learning (ML) and Evolutionary Algorithms (EAs) being the predominant approaches. Further categorization is based on the target hardware, including Field-Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), general-purpose Central Processing Units (CPUs), and Graphics Processing Units (GPUs). A notable observation was that most studies targeted image processing applications due to their tolerance for accuracy loss. By providing an overview of techniques and methods outlined in existing literature pertaining to the DSE of AxC designs, this survey elucidates the current trends and challenges in optimizing approximate designs.

Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods.

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary. In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains. In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT). This method produces results comparable to those of the WEAT, with strong correlations between the methods' bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor. Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.

Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions

The application of chatbots and Natural Language Processing (NLP) in cytology and cytopathology is an emerging field, which is currently characterized by a limited but growing body of research. Here, a narrative review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This narrative review explores the early developments and potential future impact of these technologies in medical diagnostics. The current literature, comprising 11 studies (after excluding comments, letters, and editorials) suggests that chatbots and NLP offer significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and improve patient engagement. By automating the extraction and classification of medical information, these technologies can reduce human error and increase precision. They also promise to make patient information more accessible and facilitate complex decision-making processes, thereby fostering greater patient involvement in healthcare. Despite these promising prospects, several challenges need to be addressed for the full potential of these technologies to be realized. These include the need for data standardization, mitigation of biases in Artificial Intelligence (AI) systems, and comprehensive clinical validation. Furthermore, ethical, privacy, and legal considerations must be navigated carefully to ensure responsible AI deployment. Compared to the more established fields of histology, histopathology, and especially radiology, the integration of digital tools in cytology and cytopathology is still in its infancy. Building on the advancements in related fields, especially radiology’s experience with digital integration, where these technologies already offer promising solutions in mentoring, second opinions, and education, we can leverage this knowledge to further develop chatbots and natural language processing in cytology and cytopathology. Overall, this review underscores the transformative potential of these technologies while outlining the critical areas for future research and development.

Ozonation as an emerging technique for inactivating microorganisms and mitigating pesticides in apple juice

ABSTRACT Microorganisms and pesticide residues are among the major health concerns associated with food products. Although ozone (O3) is currently being employed in various applications in apple processing, there are few studies investigating the effects of O3 treatments on microorganisms and pesticides in apple juice. The purpose of this study was to investigate the effects of O3 treatment on microorganisms, pesticide residues, and physicochemical properties of apple juice and to compare with that of classical pasteurization. O3 was as effective as pasteurization against E. coli and coliform bacteria; the counts of these microorganisms were below the detectable level after a treatment for 15 min. O3 was also effective in reducing the counts of total aerobic mesophilic bacteria and yeast-mold, but longer treatments were required for total inactivation. All the pesticides investigated were degraded by O3, but at different rates. Reduction rates were quite high (75–100%) for methoxyfenozide, dimethoate, and pyrimethanil, however, acetamiprid and imidacloprid were degraded by only 7.1% and 14.5%, respectively. O3 treatment caused drastic decreases in color and turbidity values, total phenolic content, and antioxidant activity of apple juice. Results revealed that the use of ozone in juice processing has potential as a technological solution to product contamination problem.

High-Extinction Photonic Filters by Cascaded Mach–Zehnder Interferometer-Coupled Resonators

In this study, we demonstrate high-extinction stop-band photonic filters based on Mach–Zehnder interferometer (MZI)-coupled silicon nitride (Si3N4) resonators fabricated using I-line lithography technology. Leveraging the low-loss silicon nitride waveguide, our approach enables the creation of stable, high-performance filters suitable for applications in quantum and nonlinear photonics. With destructive interference at the feedback loop, photonic filters with an extinction ratio of 35 dB are demonstrated with four cascaded MZI-coupled resonators. This cascading design not only enhances the filter’s extinction but also improves its spectral sharpness, providing a more selective stop-band profile. Experimental results agree well with the theoretical results, showing linear scaling of extinction ratios with the number of cascaded MZI-coupled resonators. The scalability of this architecture opens the possibility for further integration and optimization in complex photonic circuits, where high extinction ratios and precise wavelength selectivity are critical for advanced signal processing and quantum information applications.

THE APPLICATION OF DATA MINING AND OLAP TECHNOLOGIES IN HEALTHCARE

The article examines the application of Data Mining and real-time Online Analytical Processing (OLAP) in healthcare. The calculation of analytical indicators and solving planning problems in healthcare are complex and multi-stage processes. The extraction of knowledge from large volumes of data accumulated in databases is referred to as Data Mining. Today, databases are measured in terabytes, containing strategically significant information. One of the most critical issues is determining methods for discovering important information within such vast amounts of data. Data Mining technology, by utilizing a set of analytical tools, helps to uncover relationships within large datasets and make predictions about future trends. Simply defined, Data Mining automatically identifies relevant patterns within databases. For many years, analysts manually recorded statistically significant relationships within databases. Today, Data Mining automates this process. The goal of Data Mining is to create decision-making models that predict future behavior based on the analysis of past actions. With the advent of computers and the rise of the digital information age, there have been changes in both the scale and complexity of existing problems in terms of analytical analysis. Advances in the storage, management, and examination of data have led to the emergence of a new field that promotes the development of bioinformatics and introduces analytical analysis and computational problems in the fields of biology and medicine. Keywords: Data Mining, OLAP, database, factor analysis, healthcare network, multidimensional models.

Research on image recognition and processing application technology of unmanned vehicle based on deep learning

Abstract. Image recognition technology is critically important in various fields, including the rapidly advancing sector of autonomous vehicles. As one of the core components enabling driverless cars to perceive their environment and make informed decisions, image recognition has seen significant advancements due to deep learning. This paper focuses on the application of deep learning in image recognition for self-driving cars and explores its implications for the future of autonomous driving technology. To begin with, this paper examines the empirical evaluation of deep learning models in highway driving scenarios. By employing Convolutional Neural Networks (CNN), these models achieve high detection rates and superior accuracy in recognizing vehicles and lanes. The robustness of these models is tested under varying weather conditions and times, demonstrating their effectiveness compared to classical computer vision techniques. Next, the paper discusses radar-camera fusion technology, highlighting different fusion strategies such as data-level, feature-level, object-level, and hybrid-level. The findings suggest that while feature-level fusion excels in detecting small objects in complex scenes, hybrid-level fusion is optimal for diverse driving situations. This section provides valuable insights into the integration of multimodal data for improved object recognition and semantic segmentation. After discussing fusion technologies, the paper finally reviews the security challenges posed by adversarial attacks on deep learning-based unmanned systems.

Pathway-directed recyclable chirality inversion of coordinated supramolecular polymers

It remains challenging to elucidate the fundamental mechanisms behind the dynamic chirality inversion of supramolecular assemblies with pathway complexity. Herein, metal coordination driven assembly systems based on pyridyl-conjugated cholesterol (PVPCC) and metal ions (Ag+ or Al3+) are established to demonstrate pathway-directed, recyclable chirality inversion and assembly polymorphism. In the Ag(I)/PVPCC system, a competitive pathway leads Ag-Complex to form either kinetically controlled supramolecular polymer (Ag-SP I) or thermodynamically favored Ag-SP II, accompanied by reversible chiroptical inversion. Conversely, the Al(III)/PVPCC system displays a solvent-assisted consecutive pathway: the Al-Complex initially forms ethanol-containing Al-SP II, and subsequently converts into ethanol-free Al-SP I with opposite chiroptical performance upon thermal treatment. Moreover, stable chirality inversion in the solid state enables potential dynamic circularly polarized luminescence encryption when Ag(I)/PVPCC is co-assembled with thioflavin T. These findings provide the guidance for the dynamic modulation of chirality functionality in supramolecular materials for applications in information processing, data encryption, and chiral spintronics.

Silicon–based integrated orbital angular momentum parity sorter

Abstract A silicon–based integrated orbital angular momentum (OAM) parity sorter using two-dimensional multimode interference (2D MMI) waveguides is designed and numerically analyzed. An OAM parity sorter, which sorts OAM modes according to their parity (even or odd) is an elegant device in a broad range of OAM states processing applications including quantum gates, quantum key distribution, generation of high dimensional quantum gates, quantum information, and teleportation. The presented three–part integrated parity sorter is realized based on the self–imaging and field–splitting properties of MMI structures. Its total length is 11 444 µm. The proposed device works for OAM modes with |l| ⩽ 5. The performance of OAM sorting is investigated with the results confirming the high purity (up to 98.33% and 94.45% at even and odd ports, respectively) for the telecommunication wavelength λ 0 = 1550 nm.

Acorns: From an Ancient Food to a Modern Sustainable Resource

Acorns, the seeds of oak trees, are pivotal in the Mediterranean region, historically serving as vital sustenance for wildlife and humans. Although traditionally used for livestock, in times of scarcity, acorns were also used in human diets together with their use in traditional medicine. In this review, we explore and deepen the multifaceted significance of oak species, consolidating nutritional data while emphasizing sustainable practices for modern dietary integration and potential applications in other areas, promoting long-term ecological balance. In the Mediterranean region, notable species are Quercus ilex, Q. faginea, Q. suber, and Q. coccifera, which differ in flavor, texture, and nutritional content. Rich in carbohydrates, acorns can contain up to 50% starch as well as amino acids, proteins, and lipids. This nutritional composition favors the preparation of flour for use in bread doughs or even diverse types of processing such as roasting, which makes it a substitute for coffee. Furthermore, acorn by-products, containing tannins, find applications in leather processing. This review highlights and summarizes in detail the nutritional potential, culinary adaptability, and diverse applications of acorns, emphasizing the need to preserve and utilize natural resources sustainably. In doing so, it sheds light on the often-overlooked value of these humble seeds.

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review

ObjectiveTo conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli using digitised human lung tissue images either through automatic or semi-automatic methods. We categorised the computer vision platform into four technologies: image processing, object/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.DesignWe searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on Mycobacterium tuberculosis detection, or tuberculosis pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.ResultsSeveral studies have been conducted to develop automated and AI-assisted methods for diagnosing Mtb and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT, and the 3D microanatomy characterisation of human tuberculosis lung using μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu$$\\end{document}CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.ConclusionThe literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit Mtb infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of Mtb. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of bacilli and the other five used CT on human lung tissue scanned ex-vivo.

AI-ENHANCED PORTFOLIO MANAGEMENT: CRAFTING OPTIMAL INVESTMENT STRATEGIES FOR DIVERSE FINANCIAL MARKETS

This study investigates the transformative potential of artificial intelligence (AI) in portfolio management across diverse financial markets. It aims to develop a comprehensive framework that enhances investment strategies in developed, emerging, and frontier markets through AI techniques. The research will employ a mixed-methods approach, combining quantitative and qualitative methodologies. Initially, extensive secondary data will be collected from market reports, financial databases, and previous studies. This data will encompass market-specific characteristics, AI-based portfolio strategies, and financial indicators. AI methods, including neural networks, machine learning models, and natural language processing applications, will then be utilized to analyze portfolio performance across various market scenarios. The anticipated outcome of this study is a versatile AI-driven framework that accommodates the unique aspects of different financial markets. By optimizing asset allocation, risk management, and predictive analytics, this framework will provide portfolio managers with evidence-based recommendations to enhance the stability and performance of their investments. The study aims to demonstrate how AI can mitigate risks associated with market volatility, liquidity issues, and regulatory constraints, leading to a more resilient and data-driven approach to portfolio management on a global scale KEYWORDS: AI integration in finance, Portfolio performance metrics, Compounded Annual Growth Rate (CAGR), Volatility reduction

Wavelength Stabilization of Entangled Biphotons Using Dynamic Temperature Compensation for Quantum Interference Applications

AbstractIn this paper, a dynamic temperature compensation method is presented to stabilize the wavelength of the entangled biphoton source, which is generated on spontaneous parametric down‐conversion from a magnesium oxide doped periodically poled lithium niobate waveguide. Utilizing the dispersive Fourier transformation technique, the photon wavelength variation is monitored in case of conventional static temperature control, revealing a long‐term wavelength drift up to 556.8 pm over a 14‐h measurement period. A Hong‐Ou‐Mandel (HOM) interferometer is constructed to assess the impact on quantum applications, showing a decrease in visibility from 95.5% to 69.4%. To address this issue, a digital proportional‐integral‐differential algorithm is implemented to dynamically compensate the working temperature variation of the waveguide, thereby instantly stabilizing the wavelength to a peak‐to‐peak fluctuation of +138.05 pm/‐127.61 pm with the standard deviation being 30.49 pm. The wavelength stability shows more than a hundredfold enhancement in terms of Allan deviation, reaching at an averaging time of 10000 s. With the dynamic control in operation, the HOM interference visibility turns to stable at 96.1% ± 0.6%. The method provides a simple and accessible solution for precisely controlling and stabilizing the wavelength of entangled biphotons, thus improving performance in various quantum information processing applications.