Implementation Of Algorithm Research Articles

Automatic analysis of pottery sherds based on structure from motion scanning: The case of the Phoenician carinated-shoulder amphorae from Tell el-Burak (Lebanon)

Over the last few years, significant interest has been addressed in developing computer-based methods to document and analyze fragments of ceramics sherds in archaeology. This is because traditional manual processes do not allow for an objective, repeatable, and reproducible analysis of the large quantities of material needed to fully understand and explain human practices in various cultural contexts, such as the economy, daily life, and the material expression of religious beliefs.In that context, this paper proposes a fully digital methodology resulting from the constitution of an international research group coming from different scientific backgrounds: archaeologists with specific skills and experience in fast 3D geometry acquisition methods and researchers who developed and published the only available computer-based process for recognizing the geometric and morphological sherds features analyzed by archaeologists. The proposed methodology consists of two main parts: 1. 3D acquisition of sherds with the construction of the discrete 3D manifold model based on the Structure for Motion technologies; 2. recognition, segmentation, and dimensional characterization of morphological and geometrical features based on the codification and algorithmic implementation of the knowledge used by the archeologists in the traditional method. The method was applied to analyze a set of 133 sherds excavated at Tell el-Burak (Lebanon) to obtain, through the analysis of the namely Phoenician carinated-shoulder amphorae, new insights into the economic organization of the Phoenician homeland. The method demonstrated the potential for objectively, repeatedly, and reproducibly analyzing large quantities of sherds. Furthermore, it allowed studying sherds by generating new high-level knowledge from those acquired from 3D models; in particular, this paper introduces new morphological features that help the archaeologist classify fragments from an analysis of the rim's shape.

Association rule mining algorithm implementation for e-commerce in the retail sector

The growth of online trading platforms and the development of market technology have forced businesses to take part in the analysis of client behavior. Therefore, this research aims to analyze customer behavior in the Kyrgyz Republic to enhance supplier's revenue, service quality, and customer satisfaction. This data was analyzed using the apriori algorithm. Results generated 118 rules which revealed strong connections between items and showed up to 61.06% relationship between the consumption of products, suggesting a connection among the considered items. Thus, the association rule highlights the significance of association rule mining in uncovering valuable insights within sales transaction data. These insights can inform targeted marketing efforts, inventory management, and the enhancement of customer experiences and optimize business strategies to meet customer preferences, ultimately fostering growth and competitiveness in the retail sector.

Fisheries Inspection in Portuguese Waters from 2015 to 2023

As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas. This georeferenced dataset describes the fisheries inspections done in Portuguese waters between 2015 and 2023. Since we are dealing with occurrences that may have originated some legal process to the ship’s owner, we have ensured data anonymization by pre-processing the dataset to maintain its accuracy while guaranteeing no unique identifiers exist. All the pre-processing performed to ensure data consistency and accuracy is described in detail to allow a quick analysis and implementation of new algorithms. The data containing the results of these inspections can be easily analyzed to implement data mining algorithms that can efficiently retrieve more knowledge and, e.g., suggest new areas of actuation or new strategies.

UAV‐based simultaneous localization and mapping in outdoor environments: A systematic scoping review

AbstractThis study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)‐based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV‐based SLAM. A scoping review was carried out to identify the key concepts and applications, and discover research gaps in the use of algorithm‐oriented and task‐oriented, open‐source studies. A total of 97 studies met the criteria after conducting a two‐step screening by a systematic method followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses. Among eligible studies, 97 were classified into two main categories: algorithm‐oriented studies and task‐oriented studies. The analysis of the literature revealed that the majority of the studies were focused on the development and implementation of new algorithms and algorithms. This review highlights the significance and diversity of sensors utilized in UAVs in different tasks and applications scenarios that employ different types of sensors. The evaluation method is able to show the real results and performance of the new algorithms in the target scenarios compared with the evaluation method by the public data set and simulation platform.

A provably masked implementation of BIKE Key Encapsulation Mechanism

BIKE is a post-quantum key encapsulation mechanism (KEM) selected for the 4th round of the NIST's standardization campaign. It relies on the hardness of the syndrome decoding problem for quasi-cyclic codes and on the indistinguishability of the public key from a random element, and provides the most competitive performance among round 4 candidates, which makes it relevant for future real-world use cases. Analyzing its side-channel resistance has been highly encouraged by the community and several works have already outlined various side-channel weaknesses and proposed ad-hoc countermeasures. However, in contrast to the well-documented research line on masking lattice-based algorithms, the possibility of generically protecting code-based algorithms by masking has only been marginally studied in a 2016 paper by Chen et al. in SAC 2015. At this stage of the standardization campaign, it is important to assess the possibility of fully masking BIKE scheme and the resulting cost in terms of performances. In this work, we provide the first high-order masked implementation of a code-based algorithm. We had to tackle many issues such as finding proper ways to handle large sparse polynomials, masking the key-generation algorithm or keeping the benefit of the bitslicing. In this paper, we present all the gadgets necessary to provide a fully masked implementation of BIKE, we discuss our different implementation choices and we propose a full proof of masking in the Ishai Sahai and Wagner (Crypto 2003) model. More practically, we also provide an open C-code masked implementation of the key-generation, encapsulation and decapsulation algorithms with extensive benchmarks. While the obtained performance is slower than existing masked lattice-based algorithms, we show that masking at order 1, 2, 3, 4 and 5 implies a performance penalty of x5.8, x14.2, x24.4, x38 and x55.6 compared to order 0 (unmasked and unoptimized BIKE). This scaling is encouraging and no Boolean to Arithmetic conversion has been used.

New SAT-based Model for Quantum Circuit Decision Problem: Searching for Low-Cost Quantum Implementation

In recent years, quantum technology has been rapidly developed. As security analyses for symmetric ciphers continue to emerge, many require an evaluation of the resources needed for the quantum circuit implementation of the encryption algorithm. In this regard, we propose the quantum circuit decision problem, which requires us to determine whether there exists a quantum circuit for a given permutation f using M ancilla qubits and no more than K quantum gates within the circuit depth D. Firstly, we investigate heuristic algorithms and classical SAT-based models in previous works, revealing their limitations in solving the problem. Hence, we innovatively propose an improved SAT-based model incorporating three metrics of quantum circuits. The model enables us to find the optimal quantum circuit of an arbitrary 3 or 4-bit S-box under a given optimization goal based on SAT solvers, which has proved the optimality of circuits constructed by the tool, LIGHTER-R. Then, by combining different criteria in the model, we find more compact quantum circuit implementations of S-boxes such as RECTANGLE and GIFT. For GIFT S-box, our model provides the optimal quantum circuit that only requires 8 gates with a depth of 31. Furthermore, our model can be generalized to linear layers and improve the previous SAT-based model proposed by Huang et al. in ASIACRYPT 2022 by adding the criteria on the number of qubits and the circuit depth.

Hybrid model for speech emotion recognition of normal and autistic children (SERNAC)

Since the last decade, autism spectrum disorder (ASD) has been used as a general term to describe a wide range of conditions, including autistic syndrome, Asperger's disorder, and pervasive developmental disability. This problem emerges as a decreased ability to share emotions and a greater difficulty understanding others' feelings, leading to increased social communication difficulties. To assist patients with ASD, we proposed a concept that incorporates speech emotion detection technologies, which are widely used in the field of human-computer interaction (particularly youngsters). An algorithm based on a novel method for classifying normal and autistic children's speech emotions is implemented in this article. The training data set is treated to a new approach after all features have been extracted. The technique discussed in this study is the creation of a hybrid algorithm that serves as a classifier for normal and autistic children's speech emotions. Voice emotion recognition can be identified accurately and with a lower error rate. The data collection includes speech samples from 200 normal and 250 autistic groups in four moods (Angry, Happy, Neutral and Sad). As per research findings, the implemented hybrid algorithm for Normal and Autistic Children Speech Emotions (SERNAC) outperformed the existing classifiers by increasing accuracy.

Enhancing Milk Quality Detection with Machine Learning: A Comparative Analysis of KNN and Distance-Weighted KNN Algorithms

Ensuring the quality of milk is paramount for consumer health and industry standards. This study introduces a comparative analysis of two machine learning approaches, the k-Nearest Neighbors (KNN) algorithm and its variant, the Distance-Weighted KNN (DW-KNN), for the detection of milk quality. While the traditional KNN algorithm has been widely applied across various sectors for its simplicity and effectiveness, our research proposes an enhanced methodology through the implementation of the DW-KNN algorithm, which incorporates distance weighting to improve prediction accuracy. Through the analysis of a comprehensive dataset encompassing multiple milk quality indicators, we demonstrate that the DW-KNN algorithm significantly outperforms the standard KNN approach, achieving an exceptional accuracy of 99.53% compared to 98.58% by KNN. This substantial improvement highlights the potential of distance weighting in enhancing classification performance, particularly in applications requiring high precision in quality assessment. Our findings advocate for the adoption of the DW-KNN algorithm in the dairy industry and related fields, offering a robust tool for ensuring product quality and safety.

The green and economical supply of coated board with intermodal distribution: an implementation of novel evolutionary algorithm

The green and economical supply of coated board with intermodal distribution: an implementation of novel evolutionary algorithm

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments

This study introduces machine learning methods for automatically grading students' assignments, catering to the changing demands of education in the digital age. By utilizing diverse techniques, the aim is to simplify the grading process, give prompt feedback, and improve overall efficiency in assessment practices. The research delves into the different types of assignments such as written responses and coding tasks, and presents a range of ML algorithms including NB, Decision Trees (DT), RF, SVM, Linear Regression (LR), KNN, and Ensemble Methods (EMs). The implementation involves extracting relevant features from assignments, preprocessing data to handle noise and outliers, and training models on a varied set of examples. The grading system prioritizes interpretability, accuracy, and efficiency while aligning with educational objectives and grading policies. Evaluation is done using suitable metrics like recall, accuracy, precision, F1 score for each algorithm. The study contributes to the progress of automated grading systems by providing valuable insights for educators on the potential impact of machine learning in enhancing assessment processes. Additionally, the designed algorithms demonstrate adaptability for different types of assignments and serve as a foundation for future enhancements in grading precision and efficiency.

Machine learning and IoT based MIMO-UWB Antenna Integrated with Ku-Band for Seamless Wireless Communication

The implementation of machine learning algorithms with advanced antenna technologies, such as Multiple Input Multiple Output Ultra-Wideband -UWB- and Cup-Ban, has emerged as a powerful method for improving wireless communication systems. This research focuses on the effectiveness of applying three machine learning algorithms, which are Deep Q-Networks -DQN- , Label Propagation, and Model-Agnostic Meta-Learning (MAML). Using several experiments, we analyzed the throughput, latency, and packet loss of every algorithm. These were part of the project designed on a wireless signal combined with MIMO-UWB and Ku-Band. Nevertheless, MAML has the highest average throughput of 261.3 Mbps, and the other algorithms follow one by one: DQN with 252.5 Mbps and Label Propagation with 240.6 Mbps. As to latency, the lowest average is 9.0 ms and 11.0 ms for DQN and 14.2 ms for Label Propagation. Finally, MAML proved the least packet losses with the average value of 0.18% and DQN with 0.39% and 0.73% of Label Propagation .The results presented above indicate that by adapting more quickly to changing wireless environments, MAML effectively optimizes communication performance in terms of throughput, latency, and packet loss. Specifically, through its ability to accelerate the allocation of resources and management of networks as a whole, more frequent and rapid learning with MAML results in better system reliability and data integrity.

An efficient model for 3D analysis of steel frames exposed to fire

The paper presents a nonlinear concentrated plasticity frame element for advanced analysis of steel frames exposed to elevated temperatures. The element extends the formulation based on the generalized plasticity material model that was successfully applied to the analysis of steel and composite structures. It considers the material's nonlinear behavior, temperature-induced loading, strength and stiffness degradation typical for structures under fire conditions. The nonlinear geometry is taken into account with the corotational formulation. Because of the generalized plasticity relations adoption, the element can describe the gradual yielding of cross sections. At the same time, the implemented return mapping algorithm ensures the element's high computational efficiency. The governing element equations, selection of parameters and computer implementation are discussed in the paper, followed by element validation through the number of experimental data examples and other numerical models. The comparative analysis demonstrated the element's versatility and capability to accurately predict the structural response of steel frame structures exposed to fire.

Ensemble Learning-based Algorithms for Traffic Flow Prediction in Smart Traffic Systems

Due to the tremendous growth of road traffic accidents, Intelligent Transportation Systems (ITSs) are becoming even more important. To prevent road traffic accidents in the long term, it is necessary to find new vehicle flow management techniques in order to optimize traffic flow. With the high growth of deep learning and machine learning, these methods are increasingly being used in ITSs. This research provides a novel conceptual ITS model that aims to predict vehicle movement through the collective learning usage to anticipate intersections. The proposed approach consists of three main stages: data collection through cameras and sensors, implementation of machine learning and deep learning algorithms, and result evaluation, utilizing the coefficient of determination (R-squared), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To accomplish this, various machine learning and deep learning algorithms, such as Random Forest, LSTM, Linear Regression, and ensemble methods (bagging), were incorporated into the model. The findings revealed the enhancement due to the proposed method, which was observed through a significant performance improvement of 93.52%.

DESIGN MODELS OF BIT-STREAM ONLINE-COMPUTERS FOR SENSOR COMPONENTS

Context. Currently, distributed real-time control systems need the creation of devices that perform online computing operations close to the sensor. The proposed online-computers of elementary mathematical functions can be used as components for the functional conversion of signals in the form of pulse streams received from measuring sensors with frequency output.
 Objective. The objective of the study is the development of mathematical, architectural and automata models for the design of bit-stream online-computers of elementary mathematical functions in order to create a unified approach to their design, due to which the accuracy of calculating functions can be increased, functional capabilities expanded, hardware costs reduced, and design efficiency increased.
 Method. Mathematical models of devices were developed using the method of forming increments of ascending step functions based on inverse functions with minimization of calculation error. Automata models of online-computers based on Moore’s Finite State Machine have been developed, the graph diagrams of which made it possible to ensure the clarity of function implementation algorithms, to increase visibility and invariance of implementation in formal languages of programming and hardware description.
 Results. The paper presents the results of research, development and practical approbation of design models of bit-stream onlinecomputers of power functions and root extraction function. A generalized architecture of an online-computer was proposed.
 Conclusions. The considered functional online-computers are effective from the point of view of calculation accuracy, simplicity of technical implementation, and universality of the architecture

Implementation of the Asymmetric Fountain Code Algorithm to Secure Aviation Licensing and Regulatory Document Data

Several agencies, both government and private, require the security of personal data, especially as this information can only be accessed or transmitted to authorized people or organizations. Likewise with license and regulatory records, these materials are exceedingly confidential. Documents pertaining to permissions and regulations issued by aviation authorities, such as operating permits and aircraft certification, However, the difficulty is that when data is distributed using an internet connection, it can slip into the hands of unscrupulous people. Implementation of the Asymmetric Fountain Code Algorithm to safeguard confidential text data is used to safeguard license and regulatory document data with encryption and description methods using the Fountain Code Algorithm, which has two keys to access plaintext and ciphertext documents using SharpDevelop 5.1 software in the C# language. This research reveals that the outputs of the Fountain Code algorithm can restore plaintext data in its entirety. So it can be inferred that the asymmetric Fountain Code algorithm meets good data integrity and is safe; the complexity of the algorithm is simpler; and the amount of plaintext is exactly related to time.

Clinical viability of an active spot scanning beam delivery system with a newly developed carbon-ion treatment planning system

PurposeAlthough active spot scanning irradiation technique is theoretically superior to passive-scattered broad beam irradiation with respect to normal tissue sparing, corroborations of the clinical benefit of carbon-ion spot scanning have remained scarce. This study aims to investigate the feasibility and clinical implementation of an active spot scanning beam calculation algorithm in a homemade carbon-ion treatment planning system (TPS) by comparing it with a conventional passive uniform scanning technique. Materials and methodsCarbon-ion plans were initially formulated using spot/uniform scanning methods in 22 participants enrolled in a prospective observational clinical trial. Subsequently, two additional plans were designed, resulting in three carbon-ion plans for each participant: uniform and spot scanning with mini-ridge filters (MRFs) of 2mm and 4mm, respectively. ResultsThe findings revealed no significant differences in dose homogeneity; however, significant differences in dose conformity were found between the active and passive scanning plans. For dose drop-off outside the target volume, the average gradient index (GI) values were 1.94 (95% confidence interval (CI): 1.79%-2.09%), 1.87 (95% CI: 1.73%-2.01%), and 3.20 (95% CI: 2.80%-3.61%) for the MRF4, MRF2, and uniform scanning plans, respectively. The pre-treatment tumor volume was 124.7 cm3 (range, 54.2-234 cm3), and the average shrinkage observed was 38.4% (95% CI: 17.6%-59.4%). Seven participants experienced grade 1 acute toxicity, and four experienced grade 2 acute toxicity. However, none of the patients developed grade 3 acute toxicity. ConclusionsIncreasing evidence suggests that potential clinical advantages of spot scanning delivery underlie its technical characteristics. As one among the few institutions currently utilizing carbon-ion radiotherapy, the investigation also provides promising safety and efficacy outcomes from the initial groups of treated participants, thereby contributing to the established clinical evidence supporting the effectiveness and superiority of carbon-ion therapy.

Identifying synergies between VLBI and STIX imaging

Context. Reconstructing an image from noisy, sparsely sampled Fourier data is an ill-posed inverse problem that occurs in a variety of subjects within science, including data analysis for Very Long Baseline Interferometry (VLBI) and the Spectrometer/Telescope for Imaging X-rays (STIX) with respect to solar observations. The need for high-resolution, high-fidelity imaging fosters the active development of a range of novel imaging algorithms in a variety of different algorithmic settings. However, despite these ongoing, parallel developments, such synergies remain unexplored. Aims. We study, for the first time, the synergies between the data analysis for the STIX instrument and VLBI. In particular, we compare the methodologies that have been developed in both fields and evaluate their potential. In this way, we identify key trends in the performance of several algorithmic ideas and draw recommendations for the future spending of resources in the study and implementation of novel imaging algorithms. Methods. To this end, we organized a semi-blind imaging challenge with data sets and source structures that are typical for sparse VLBI, specifically in the context of the Event Horizon Telescope (EHT) as well as STIX observations. We used 17 different algorithms from both communities, from six different imaging frameworks, in the challenge, making this work the largest scale code comparison for STIX and VLBI to date. Results. We identified strong synergies between the two communities, as proven by the success of the imaging methods proposed for STIX in imaging VLBI data sets and vice versa. Novel imaging methods outperform the standard CLEAN algorithm significantly in every test case. Improvements over the performance of CLEAN offer deeper updates to the inverse modeling pipeline necessary or, consequently, the possibility to replace inverse modeling with forward modeling. Entropy-based methods and Bayesian methods perform best on STIX data. The more complex imaging algorithms utilizing multiple regularization terms (recently proposed for VLBI) add little to no additional improvements for STIX. However, they do outperform the other methods on EHT data, which correspond to a larger number of angular scales. Conclusions. This work demonstrates the great synergy between the STIX and VLBI imaging efforts and the great potential for common developments. The comparison identifies key trends on the efficacy of specific algorithmic ideas for the VLBI and the STIX setting that may evolve into a roadmap for future developments.

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP features.

Epidemiology of NAFLD – Focus on diabetes

There is increasing appreciation of the complex interaction between nonalcoholic fatty liver disease (NAFLD) with type 2 diabetes (T2D) and insulin resistance. Not only is the prevalence of NAFLD disease high among patients with T2D, the liver disease is also more progressive. Currently, the global prevalence of NAFLD in the general population (2016–2019) is 38 %. The prevalence of T2D among those with NAFLD is approximately 23 % while the prevalence of NAFLD among those with T2D can be as high as 70 %. The prevalence of nonalcoholic steatohepatitis (NASH) is approximately 7 % in the general population and 37 % among patients with T2D. Globally, the MENA and Latin America regions of the world appear to have the highest burden of both NAFLD and T2D. Compared to those with NAFLD but without T2D, those with NAFLD and T2D are at a much higher risk for disease progression to cirrhosis and for decompensated cirrhosis, hepatocellular carcinoma, and all-cause mortality. Given that highly effective new treatments are available for T2D, high risk NAFLD with T2D should be considered for these regimens. This requires implementation of risk stratification algorithms in the primary care and endocrinology practices to identify those patients at highest risk for adverse outcomes.