Development Of Advanced Algorithms Research Articles

Effectiveness of the Implementations of a Data Mining Algorithm Based Peak Restriction Method

The rapid growth of data in various domains has necessitated the development of advanced algorithms to efficiently analyze and manage large datasets. One of the key challenges in data mining is dealing with peak values or outliers, which can distort the analysis and lead to inaccurate predictions or decisions. This paper investigates the effectiveness of a data mining-based peak restriction method designed to minimize the influence of extreme values in large datasets. The proposed method uses a combination of data preprocessing techniques and machine learning algorithms to identify and limit peak values, enhancing the robustness and accuracy of predictive models. We evaluate the method across different datasets, including time-series, transactional, and sensor data, to assess its generalizability and performance. The results demonstrate significant improvements in model accuracy and stability, especially in applications sensitive to outliers, such as anomaly detection and predictive maintenance. Our findings suggest that the peak restriction method is a promising tool for improving the quality of data-driven models, with potential applications in various industries including finance, healthcare, and IoT. Furthermore, we discuss the limitations of the approach and propose future directions for further optimization.

The Role of AI and Machine Learning in Revolutionizing Prenatal Screening and Genetic Analysis

rtificial Intelligence (AI) and Machine Learning (ML) are at the forefront of innovations in medical diagnostics, including prenatal screening and genetic analysis. The development of advanced algorithms, data processing capabilities, and predictive modeling has significantly enhanced the sensitivity, specificity, and accuracy of non-invasive prenatal testing (NIPT). This article explores recent advancements in AI- driven prenatal screening, the methodologies employed, and the future potential of AI in predictive prenatal health diagnostics, with a particular focus on improving genetic disorder detection and fetal health outcomes. The role of ethical considerations in AI-driven diagnostics is also discussed.

Towards Complex-query Referring Image Segmentation: A Novel Benchmark

Referring Image Segmentation (RIS) has been extensively studied over the past decade, leading to the development of advanced algorithms. However, there has been a lack of research investigating how existing algorithms should be benchmarked with complex language queries, which include more informative descriptions of surrounding objects and backgrounds ( e.g. “the black car.” vs. “the black car is parking on the road and beside the bus.” ). Given the significant improvement in the semantic understanding capability of large pre-trained models, it is crucial to take a step further in RIS by incorporating complex language that resembles real-world applications. To close this gap, building upon the existing RefCOCO and Visual Genome datasets, we propose a new RIS benchmark with complex queries, namely RIS-CQ . The RIS-CQ dataset is of high quality and large scale, which challenges the existing RIS with enriched, specific and informative queries, and enables a more realistic scenario of RIS research. Besides, we present a nichetargeting method to better task the RIS-CQ, called dual-modality graph alignment model ( DuMoGa ), which outperforms a series of RIS methods. To provide a valuable foundation for future advancements in the field of RIS with complex queries, we release the datasets, preprocessing and synthetic scripts, and the algorithm implementations at https://github.com/lili0415/DuMoGa .

Nighttime Pothole Detection: A Benchmark

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety.

Artificial Intelligence in Healthcare: Implications, Challenges, and Future Prospects

Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities. This technology is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes. Just like a physician, AI can record a patient's history, signs and symptoms, and lab results, leading to an accurate diagnosis and a proper treatment plan, but in no time. This is just a small example of the implications of AI in the sector. Other areas of medicine are also being benefited by this innovation, like gastroentology, radiology, surgery, and preventive medicine. The development of advanced algorithms has reduced the burden on radiologists by helping them identify abnormal images and pick up malignant lesions with minimal diagnostic errors. Similarly, AI-assisted colonoscopy can help identify malignant and benign polyps. AI-powered systems, like Google's DeepMind Health, may identify malignant growth in mammograms and diabetic retinopathy, which can aid in early detection and treatment.

Open Remote Sensing Data in Digital Soil Organic Carbon Mapping: A Review

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations’ Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping.

Influence of Viral Contents on the Rapid Spread of Information on the Social Media Platforms in Nigeria

This paper highlighted the influence of viral content on the rapid spread of information on the social media platforms. This paper raised four (4) research questions as guide. The descriptive survey design was used in this investigation. The study population was three hundred and fifty (350) social media users. The sample size of one hundred and thirty (130) social media content creator and users was adopted relying on the simple random sampling technique. The questionnaire was used as the instrument for collecting data. Descriptive statistics specifically tables and mean were used for the analysis of data collected. Findings shows that emotional connection, visual appeal, relevance, social proof, convenience, controversy, novelty, humour, timeliness and interactive elements are the factors that contribute to the rapid spread of viral content on social media platforms. The paper concluded that the potential consequences of the rapid spread of viral content to individuals and society include dissemination of false information which causes harm to individuals and society, feelings of anxiety and stress, increased social media addiction, negative impact on mental health for some individuals, anxious feelings, and stress due to the constant stream of alarming information. This paper recommended that platforms should establish, enforce clear, stringent content moderation policies that address the unique challenges posed by viral content and development of advanced algorithms that should be regularly updated to adapt to evolving tactics used by those spreading false information, which should work in tandem with human review processes to ensure a balanced approach.

Emerging Trends in Applied Mathematics

This research explores the emerging trends in applied mathematics and their far-reaching implications in various fields. Machine learning and artificial intelligence are revolutionizing healthcare, finance, and natural language processing. Big data analysis is enhancing decision-making in finance, healthcare, and logistics. Quantum computing promises to transform materials science and renewable energy. These trends are reshaping research and practice, offering innovative solutions and opportunities for interdisciplinary collaboration. Ethical considerations and the development of advanced algorithms are critical areas of future research. This study serves as a foundation for understanding and harnessing the potential of these trends, emphasizing the importance of continuous exploration and skill development in an evolving mathematical landscape.

Using QoE Metric as a Decision Criterion in Multimedia Heterogeneous Network Optimization: Challenges and Research Perspectives

This article explores the growing importance of the QoE (Quality of Experience) metric as a fundamental criterion in the optimization of heterogeneous multimedia networks. We explore the benefits of using QoE, such as improving user experience, efficient resource management, and adaptation to network conditions. However, this paradigm presents technical challenges, including accurately measuring QoE and managing the complexity of heterogeneous networks. To address these challenges, we highlight promising research prospects, such as the development of advanced algorithms, real-time measurement of QoE, and the integration of machine learning. Ultimately, the use of QoE in the optimization of heterogeneous multimedia networks contributes to a substantial improvement in the quality of multimedia services offered to end users.

A Review on Enhancing Accessibility Through Image and Video Processing: Solutions for Differently Abled Individuals

This paper presents innovative methodologies in image and video processing aimed at augmenting accessibility for differently abled individuals. Central to this research is the development of advanced algorithms that enable enhanced interpretation and interaction with multimedia content, thereby empowering users with sensory impairments. The study introduces a multi-layered framework that integrates adaptive filtering, object recognition, and augmented reality, tailored to the needs of users with visual and auditory challenges. Semantic scene analysis is leveraged to provide descriptive audio annotations for the visually impaired, facilitating a comprehensive understanding of visual data. For individuals with hearing impairments, the system incorporates real-time sign language interpretation within videos, utilizing deep learning techniques. The efficacy of these solutions is measured against conventional accessibility tools, demonstrating significant improvements in user engagement and comprehension. A novel contribution of this research is the application of machine learning to calibrate the system according to individual user profiles, ensuring a personalized and intuitive user experience. The scalability of the proposed system is validated through its implementation across various platforms and content formats. The findings suggest that such technological advancements have the potential to significantly reduce the barriers faced by differently abled individuals in accessing multimedia information.

Development and implementation of a MATLAB-based phasor data concentrator for synchrophasor applications

This work presents the development of MatPDC, an IEEE Std. C37.118.2-2011 compliant Phasor Data Concentrator (PDC) implemented in the Matlab environment. MatPDC enables the integration of wide-area monitoring, protection and control system synchrophasor data into the Matlab platform, offering real-time access to synchronized data streams from multiple Phasor Measurement Units (PMUs). The key features of MatPDC include real-time data access, data integrity verification, data aggregation and alignment, compliant data communication, and latency calculation. These features facilitate efficient processing and analysis of synchronized data, reducing complexity and improving latency between data sources and applications. The development of MatPDC addresses the need for accessing and analyzing real-time synchrophasor data within the Matlab environment, providing researchers and engineers with a powerful tool for power system analysis and experimentation. By developing the PDC and synchrophasor applications on the same platform, MatPDC reduces complexity and latency between data sources and applications. It offers real-time access to synchronized data streams, ensuring data integrity through verification mechanisms and aggregating the data into a unified dataset based on time tags. The implementation of MatPDC opens up opportunities for researchers to work with real-time synchrophasor data. It facilitates the development of advanced algorithms, real-time simulations, and the verification of control strategies. The integration and evaluation of MatPDC demonstrate its effectiveness and potential in power system analysis, providing researchers and engineers with a valuable tool for their research and development activities.

DATA FUSION OF ULTRASONIC SENSORS FOR DISTANCE MEASUREMENT

Distance measurement plays a fundamental role in contemporary technology, exerting a significant influence on applications across a myriad of sectors, ranging from autonomous navigation and industrial automation to robotics. Ultrasonic sensors, celebrated for their simplicity, cost-efficiency, and adaptability, have grown into essential instruments for gauging distances. Nevertheless, individual ultrasonic sensors are not exempt from constraints, which encompass inaccuracies stemming from environmental conditions, sensor noise, and measurement errors. This article delves into the advanced practice of data fusion, a method that entails amalgamating data from multiple ultrasonic sensors, bolstered by intricate algorithms like artificial intelligence (AI) and statistical techniques. The fusion of ultrasonic sensor data surpasses mere amalgamation; it harnesses the potency of AI to refine data integration. Consequently, this doesn't only enhance measurement precision but also equips systems to function dependably in dynamic and demanding environments. The article explores the importance of ultrasonic sensors, the necessity for data fusion, procedures for data preprocessing, the extraction of relevant features, the application of fusion algorithms, and the handling of uncertainties. It investigates the utilization of ultrasonic sensor data fusion in diverse domains such as autonomous vehicles, robotics, industrial automation, healthcare, and environmental monitoring. Moreover, it confronts issues associated with data quality, environmental variations, privacy concerns, and ethical deliberations. The article concludes by shedding light on the encouraging avenues that lie ahead in the field, which include the development of advanced algorithms, the seamless integration of different sensor types, real-time data processing, and a strong emphasis on safety-critical applications. Data fusion from ultrasonic sensors signifies an evolving and transformative technology with profound consequences for various sectors. The responsible advancement and deployment of this technology are of paramount importance to ensure the realization of its full potential, while maintaining the principles of safety, ethics, and reliability in a continuously evolving technological landscape. Keywords: Complex measurement, Multi-sensor distance measurement, Ultrasonic sensors, Data fusion, Measurement accuracy.

Enhancing Pest Detection: Assessing Tuta absoluta (Lepidoptera: Gelechiidae) Damage Intensity in Field Images through Advanced Machine Learning

Tomato (Solanum lycopersicum (Solanaceae)), susceptible to significant yield loss due to pests, is particularly impacted by Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), a pest that directly and profoundly influences tomato yields. Consequently, early detection of T. absoluta damage intensity on leaves using machine learning or artificial intelligence-based algorithms is crucial for effective pest control. In this ground-breaking study, the galleries generated by T. absoluta were examined via field images using the Decision Trees (DTs) algorithm, a machine learning method. The unique advantage of DTs over other algorithms is their inherent capacity to identify complex and vague shapes without the necessity of feature extraction, providing a more streamlined and effective approach. The DTs algorithm was meticulously trained using pixel values from the leaf images, leading to the classification of pixels within regions with and without galleries on the leaves. Accordingly, the gallery intensity was determined to be 9.09% and 35.77% in the test pictures. The performance of the DTs algorithm, as evidenced by a high coefficient of determination (R²) and an accuracy rate of 0.98 and 0.99 respectively, testifies to its robust predictive and classification abilities. This pioneering study has far-reaching implications for the future of precision agriculture, potentially informing the development of advanced algorithms that can be integrated into autonomous vehicles. The integration of DTs in such applications, thanks to their distinctive ability to handle complex and indistinct shapes without the need for feature extraction, sets the stage for a new era of efficient and effective pest control strategies.

A deep learning-enhanced framework for multiphysics joint inversion

Joint inversion has drawn considerable attention due to the availability of multiple geophysical data sets, ever-increasing computational resources, the development of advanced algorithms, and its ability to reduce inversion uncertainty. A key issue of joint inversion is to develop effective strategies to link different geophysical data in a unified mathematical framework, in which the information obtained from different models can complement each other. We have developed a deep learning-enhanced joint inversion framework to simultaneously reconstruct different physical models by fusing different types of geophysical data. Traditionally, structure similarity constraints are pursued by joint inversion algorithms using manually crafted formulations (e.g., cross gradient). The constraint is constructed by a deep neural network (DNN) during the learning process. The framework is designed to combine the DNN and a traditional independent inversion workflow and improve the joint inversion result iteratively. The network can be easily extended to incorporate multiphysics without structural changes. Numerical experiments on the joint inversion of 2D DC resistivity data and seismic traveltime are used to validate our method. In addition, this learning-based framework demonstrates excellent generalization abilities when tested on data sets using different geologic structures. It also can handle different sensing configurations and nonconforming discretization.

TIAToolbox as an end-to-end library for advanced tissue image analytics

BackgroundComputational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers.MethodsBy creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models.ResultsWe demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort.ConclusionsWe provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature.

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey.

Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks.

Oculogica: An Eye-Catching Innovation in Health Care and The Privacy Implications Of Artificial Intelligence and Machine Learning in Diagnostics For The Human Brain

This article explores the use of Artificial Intelligence (AI) in emerging eye-tracking diagnostic technology, with a focus on both the patient data privacy and security regulations that firms, specifically device inventors and manufacturers, may face and how such firms can address the developing privacy and regulatory legal challenges. In addition, we discuss the ethical considerations of algorithmic bias, the impact such biases have on society and emerging technology, along with specific actions companies should take to maximize patient outcomes. Lastly, we offer a case study of Oculogica, an emerging digital health technology company—and its medical device (EyeBOX) – to illustrate how digital health firms can enhance patient outcomes, while ensuring data security and privacy, while simultaneously promoting responsible development of advanced algorithms for diagnostic AI.

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

Real-Time Shape Estimation for Concentric Tube Continuum Robots with a Single Force/Torque Sensor.

Shape-sensing in real-time is a key requirement for the development of advanced algorithms for concentric tube continuum robots when safe interaction with the environment is important e.g., for path planning, advanced control, and human-machine interaction. We propose a real-time shape-estimation algorithm for concentric tube continuum robots based on the force-torque information measured at the tubes’ basis. It extends a shape estimation algorithm for elastic rods based on discrete Kirchhoff rod theory. For simplicity and efficiency of calculation, we combine it with a model under piece-wise constant curvature assumption, in which we model a concentric tube continuum robot as a combination of segments of planar constant curvatures lying on different equilibrium planes. We evaluate our approach for a single and two combined additively manufactured tubes and achieve an estimation frequency of 333 Hz for two combined tubes with a mean deviation along the backbone of the tubes of 1.91–5.22 mm.

Construction of a Wireless-Enabled Endoscopically Implantable Sensor for pH Monitoring with Zero-Bias Schottky Diode-based Receiver.

Ambulatory pH monitoring of pathological reflux is an opportunity to observe the relationship between symptoms and exposure of the esophagus to acidic or non-acidic refluxate. This paper describes a method for the development, manufacturing, and implantation of a miniature wireless-enabled pH sensor. The sensor is designed to be implanted endoscopically with a single hemostatic clip. A fully passive rectenna-based receiver based on a zero-bias Schottky diode is also constructed and tested. To construct the device, a two-layer printed circuit board and off-the-shelf components were used. A miniature microcontroller with integrated analog peripherals is used as an analog front end for the ion-sensitive field-effect transistor (ISFET) sensor and to generate a digital signal which is transmitted with an amplitude shift keying transmitter chip. The device is powered by two primary alkaline cells. The implantable device has a total volume of 0.6 cm3 and a weight of 1.2 grams, and its performance was verified in an ex vivo model (porcine esophagus and stomach). Next, a small footprint passive rectenna-based receiver which can be easily integrated either into an external receiver or the implantable neurostimulator, was constructed and proven to receive the RF signal from the implant when in proximity (20 cm) to it. The small size of the sensor provides continuous pH monitoring with minimal obstruction of the esophagus. The sensor could be used in routine clinical practice for 24/96 h esophageal pH monitoring without the need to insert a nasal catheter. The "zero-power" nature of the receiver also enables the use of the sensor for automatic in-vivo calibration of miniature lower esophageal sphincter neurostimulation devices. An active sensor-based control enables the development of advanced algorithms to minimize the used energy to achieve a desirable clinical outcome. One of the examples of such an algorithm would be a closed-loop system for on-demand neurostimulation therapy of gastroesophageal reflux disease (GERD).