Hamming Distance Research Articles

DeBERTa-BiLSTM: A multi-label classification model of Arabic medical questions using pre-trained models and deep learning

It is wise to investigate past and present epidemics in the hopes of profiting from them and being better prepared for future ones. COVID-19 is one of the most recent and well-known pandemics; its effects are still felt today. Most or nearly all governments have announced various measures to combat the virus, making it challenging to keep people aware of the most up-to-date and relevant information. As a result, many websites have created and maintained Frequently Asked Questions (FAQs) regarding the pandemic. People naturally tend to ask about multiple points in one question, leading to multi-label questions. Multi-label questions classification is one of Natural Language Processing’s (NLP) most common and complicated tasks. One of classification’s most significant contributions to advancing medical care and facilities is the development of automated question-and-answer systems. These systems can improve the efficiency of healthcare by reducing the burden on healthcare professionals and providing patients with timely and reliable answers to their questions. Due to the Arabic language’s intricate morphology and structure, such a task becomes more challenging when dealing with Arabic text. This study aims to build a multi-label classification model for Arabic medical questions. The investigation of pre-trained neural models significantly improved NLP performance. Recently, pre-trained models have been used in multi-label classification. This study proposes a deep learning model for classifying Arabic multi-label COVID-19 questions by combining the strengths of DeBERTa (Decoding-enhanced BERT with Disentangled Attention) and BiLSTM (Bidirectional Long Short-Term Memory) networks. Deep learning methods are prevalent because they generate dense feature representations automatically and implicitly capture hidden relationships. The DeBERTa model is fine-tuned to generate the representation of word vectors. The BiLSTM model is fed word vectors to extract and represent features deeply. The proposed multi-label classification model categorizes questions into one or more available ten categories. The deep learning model is evaluated using hamming loss, micro-precision, micro-recall, micro-F1, subset accuracy, AUC, and Jaccard index. It showed an effective classification for Arabic questions with encouraging performance. The proposed model achieved values of 0.042 for hamming loss, 0.84 for micro-precision, micro-recall, and micro-F1, 0.71 for subset accuracy, 0.89 for AUC, and 0.72 for Jaccard index. Therefore, this paves the way for adopting an automated multi-label classification model for medical questions in health facilities. Which can help telehealth medical providers present more reliable and effective consultations.

Multi-label feature selection with high-sparse personalized and low-redundancy shared common features

Prevalent multi-label feature selection (MLFS) approaches to obtain the most suitable feature subset by dealing with two issues, namely sparsity and redundancy. In this paper, we design an efficient Elastic net based high Sparse personalized and low Redundancy Feature Selection approach for multi-label data named ESRFS to address the two obstacles, i.e., low-sparse LASSO-norm leads to personalized features for each label while high redundancy l2,1-norm explore shared common features for all labels in multi-label learning. These two problems impede the selection of high-quality features for classification. In comparison with previous MLFS approaches, ESRFS has two main superiority. First, ESRFS achieves higher sparse personalized features than LASSO-norm. Second, ESRFS can identify low redundancy shared common features with strong discrimination by introducing a novel regularization term. To effectively and efficiently identify the most optimal feature subset, an alternating-multiplier-based rule is introduced to optimize ESRFS. Experimental results on fifteen multi-label data sets show that ESRFS can achieve obvious superior performance compared to eight state-of-the-art MLFS approaches in 80%, 80%, 73.3%, 80%, 86.7%, 80% cases based on Hamming Loss, Zero-One Loss using MLkNN, Micro-F1 and Macro-F1 using SVM as well as Micro-F1 and Macro-F1 using 3NN perspectives.

ZWNet: A Deep-Learning-Powered Zero-Watermarking Scheme with High Robustness and Discriminability for Images

In order to safeguard image copyrights, zero-watermarking technology extracts robust features and generates watermarks without altering the original image. Traditional zero-watermarking methods rely on handcrafted feature descriptors to enhance their performance. With the advancement of deep learning, this paper introduces “ZWNet”, an end-to-end zero-watermarking scheme that obviates the necessity for specialized knowledge in image features and is exclusively composed of artificial neural networks. The architecture of ZWNet synergistically incorporates ConvNeXt and LK-PAN to augment the extraction of local features while accounting for the global context. A key aspect of ZWNet is its watermark block, as the network head part, which fulfills functions such as feature optimization, identifier output, encryption, and copyright fusion. The training strategy addresses the challenge of simultaneously enhancing robustness and discriminability by producing the same identifier for attacked images and distinct identifiers for different images. Experimental validation of ZWNet’s performance has been conducted, demonstrating its robustness with the normalized coefficient of the zero-watermark consistently exceeding 0.97 against rotation, noise, crop, and blur attacks. Regarding discriminability, the Hamming distance of the generated watermarks exceeds 88 for images with the same copyright but different content. Furthermore, the efficiency of watermark generation is affirmed, with an average processing time of 96 ms. These experimental results substantiate the superiority of the proposed scheme over existing zero-watermarking methods.

A Study of High-Speed Hamming Distance Detection Circuit Utilizing a Neuron CMOS Inverter

This study tackles the challenge of optimizing associative memory for efficient data retrieval from large databases, crucial in real-time processing. The authors specifically address the issue of increased detection time in the minimum Hamming distance search associative memory, particularly as the number of data bits grows. This memory system utilizes Hamming distance as a key metric to identify the most similar reference data. Our contribution is the development of a new Hamming distance detection circuit employing neuron Complementary Metal Oxide Semiconductor (CMOS) inverters. This proposed circuit significantly outperforms existing models in terms of operational speed. The effectiveness and improved performance of the circuit are validated through simulations using HSPICE, a type of Simulation Program with Integrated Circuit Emphasis (SPICE) demonstrating its potential for more efficient real-time data retrieval applications.

Cosine trigonometric rules applied to average and geometric aggregating operators using extension q-rung interval-valued neutrosophic set approach.

We introduce the concept of cosine trigonometric q-rung Diophantine neutrosophic interval-valued set (CosTq-rung DioNSIVS). The fact that CosTq-rung DioNSIVS combines q-rung neutrosophic interval-valued set, q-rung neutrosophic set and neutrosophic interval-valued set is one of its distinguishing characteristics. A new idea of CosTq-rung DioNSIVWA, CosTq-rung DioNSIVWG, GCosTq-rung DioNSIVWA and GCosTq-rung DioNSIVWG is proposed in this study. We also look at the idempotency, boundedness, commutativity, and monotonicity of the CosTq-rung DioNSIVS based on algebraic operations. We considered new kinds of two distances in the proposed models, besides Euclidean and Hamming distances. The CosTq-rung DioNSIVS method was used to analyze the cosine trigonometric aggregation procedures. The study's concluding results include several fascinating and captivating discoveries.

New type of Diophantine neutrosophic aggregation operators and its extension

We introduce the concept of new type of Diophantine neutrosophic set. A Diophatine neutrosophic set is the new type of a neutrosophic set and Diophatine fuzzy set (DioFS). We discuss Diophantine neutrosophic weighted averaging (DioNWA), Diophantine neutrosophic weighted geometric (DioNWG), generalized Diophantine neutrosophic weighted averaging(GDioNWA), generalized Diophantine neutrosophic weighted geometric (GDioNWG). In this article, we define the Euclidean distance (ED), Hamming distance (HD) and operator laws. By analyzing new type of Diophantine neutrosophic set through algebraic operations, we discuss its properties.

Evaluation of Sustainable Potential Bearing Capacity of Tourism Environment Under Uncertainty: A Multiphase Intuitionistic Fuzzy EDAS Technique Based on Hamming Distance and Logarithmic Distance Measures

Evaluation of Sustainable Potential Bearing Capacity of Tourism Environment Under Uncertainty: A Multiphase Intuitionistic Fuzzy EDAS Technique Based on Hamming Distance and Logarithmic Distance Measures

A study on the application of quantum computing in improving the efficiency of English language learning

Abstract The rapid development of information technology has brought new opportunities to the field of language learning and formed new learning methods. In this paper, a content-based quantum recommendation algorithm is first proposed to calculate the preference attributes of English learners using the Hamming distance and then combined with the constructed dynamic interest model of learners to complete the personalized recommendation of English language learning resources. The algorithm is employed to build an English-language customized learning system that incorporates learning, resource management, and proficiency assessment functions, and performance testing is carried out simultaneously. Furthermore, the study utilizes the system to conduct application experiments to evaluate its improvement in English language learning efficiency. The results showed that the mean value of perceived satisfaction with the improvement of English language efficiency in classes using the system was 7.48, and the ANOVA results (F=3.88, p<0.5) indicated a significant difference from the traditional method. Among them, the performance enhancement rates of superior and intermediate students were more than 45% and 62%, respectively, which effectively enhanced the efficiency of English language learning. After this study, it can provide a high reference value for the application of quantum machine algorithms in the field of education.

Logarithmic Pythagorean neutrosophic vague aggregating operators and their real-life applications

This article examines Pythagorean neurosophic vague set (PyNVS) problems relevant to multiple attribute decision-making (MADM). Pythagorean vague set (PyVS) and neutrosophic set (NS) can be generalized into Pythagorean neutrosophic vague set (PyNVS). We discuss log Pythagorean neutrosophic vague weighted averaging (log PyNVWA), logarithmic Pythagorean neutrosophic vague weighted geometric (log PyNVWG), log generalized Pythagorean neurosophic vague weighted averaging (log GPyNVWA) and log generalized Pythagorean neutrosophic vague weighted geometric (log GPyNVWG). In this article, we define the Euclidean distance (ED), Hamming distance (HD), operator laws, and flowchart using an algorithm. By analyzing log PyNVS through algebraic operations, we discuss its properties. They can identify the best option more quickly and understand the practicalities better. An illustrative example of this is the fusion of computer science and machine tool technology in agriculture. Furthermore, there are autonomous robot tractors and soil sterilization robots that can harvest crops, weed, and take photos of seed planting with seedlings. A random selection of five farmers (alternatives) has been made. Climate, water, soil, disease, and flooding are all criteria to consider when choosing a farmer. Our goal is to narrow down the options by comparing expert judgments with the criteria.

Data‐Driven Decision Algorithm for Open Caisson Foundation Construction

The accuracy of instructions for the extraction of large open caissons directly affects the construction quality and safety. This study is focused on developing a smart construction methodology for predicting open caisson excavation instructions. The proposed approach aims to reduce the errors in excavation instructions attributable to the subjective and varying experiences of decision‐makers. The multilabel classification task of predicting the instructions of large open caisson excavation is accomplished through the problem analysis. The study conducted a comparative analysis of a multilayer perceptron model, a classifier chain model, and a multilabel K‐nearest neighbor model (MLKNN) based on Hamming loss and accuracy. The results revealed that MLKNN exhibits significant differences from the other models and is most suitable for predicting open caisson excavation instructions. Furthermore, the traditional MLKNN model was improved by refining the normalization and distance measurement methods for the open caisson scenario. The prediction accuracy of the improved MLKNN model is 89.04% for the excavation instructions and 98.63% for the nonexcavation decisions. The successful prediction of excavation instructions for a large open caisson can compensate for the lack of data mining capability of the existing monitoring system. This can reduce construction risks. Finally, this study discussed the robustness of the improved MLKNN in a real project, highlighting the model’s high accuracy during the midstage of open caisson construction.

Multi-Modal Hashing for Efficient Multimedia Retrieval: A Survey

With the explosive growth of multimedia contents, multimedia retrieval is facing unprecedented challenges on both storage cost and retrieval speed. Hashing technique can project the high-dimensional data into compact binary hash codes. With it, the most time-consuming semantic similarity computation during the multimedia retrieval process can be significantly accelerated with fast Hamming distance computation, and meanwhile the storage cost can be reduced greatly by the binary embedding. In the light of this, multi-modal hashing has recently received considerable attention to support large-scale multimedia retrieval. Different from uni-modal hashing, the multi-modal hashing focuses on modeling the multi-modal semantics and further preserving them into binary hash codes with hash learning. In this paper, we first systematically review the existing learning to hash methods for efficient multimedia retrieval, categorizing them according to the multimedia retrieval tasks, the specific multi-modal semantic modeling techniques, and hash learning strategies. Thereafter, we present the performance comparison results. We ultimately discuss the challenges and potential research directions that may require further investigation in multi-modal hash learning. To facilitate the research on multi-modal hashing, we develop an open-source performance comparison tool at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BMC-SDNU/Hashing-Retrieval</uri> .

The incidence of some critical cognitive factors that prevent change of order in university students: Uncertainty analysis

School performance depends on cognitive impact while academic performance depends on how the qualitative and quantitative evaluation of students is carried out, which is open to a wide range of very complex criteria. Students? emotional intelligence is put to the test by the intense and constant completion of tasks to achieve the required credits. However, students do not manage to adapt to this system, which causes rejection and procrastination in completing tasks, and is evident from their really bad state of mind and stress intolerance. These factors can be so strong that they prevent adequate development; school commitments and obligations are neglected, affecting academic performance, which remains in the first order. Consequently, we aim to identify the critical cognitive factors that prevent the change of order and process them through fuzzy cognitive maps. To do so, a survey of students and teachers from Spanish and Mexican universities was carried out. Through confirmatory factor analysis, the number of items to be analysed was reduced. The variance analysis detected significant differences between students? attitude and what teachers thought, and an assignment matrix was obtained. Finally, by obtaining Hamming distance, the critical factors that prevent good academic performance could be found. The main result obtained is that the critical factors that prevent change of order in cognitive elements are mainly problem solving, stress tolerance, reality testing, empathy, self-concept and happiness.

Optimizing Text Clustering Efficiency through Flexible Latent Dirichlet Allocation Method: Exploring the Impact of Data Features and Threshold Modification

A parallel corpus comprising Croatian EU legislative documents automatically translated into English spans 28 years and is enriched with metadata, including creation year and hierarchical classifier tags denoting descriptors, document types, and fields. However, nearly two-thirds of the approximately 1.5 thousand texts lack complete metadata, necessitating labor intensive manual efforts that pose challenges for human administration. This incompleteness issue can be observed in the case of official legal sites functioning as regular service provisioning databases. In response, this paper introduces an artificial cognitive and multilabel classification approach to expedite the tagging process with only a fraction of the manual effort. Leveraging the Latent Dirichlet Allocation (LDA) algorithm, our method assigns field values or tags to incompletely labeled documents. We implement a Flexible LDA variant, incorporating the influence of topics close to the most probable topic, regulated by a relative probability threshold (RPT). We evaluate the LDA prediction's dependence on document prefiltering and RPT values. Furthermore, we investigate the dependence of quantitative linguistic properties on the type and speciality of pre-processing tasks. Our algorithm, built on error-correcting optimizing codes, succesfully predicts a mixture of topic probabilities for these legal texts. This prediction is achieved by calculating the Hamming distance of binary feature vectors created using the legal fields of the EUROVOC multilingual thesaurus.

A novel processor for dynamic evolution of constrained SAT problems: The dynamic evolution variant of the discrete Hopfield neural network satisfiability model

The Boolean satisfiability problem, a renowned NP-complete challenge in computer science, has recently garnered interest in the Discrete Hopfield Neural Network - Satisfiability model. This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. However, it faces efficiency challenges when dealing with SAT problems characterized by dynamic evolution constraints due to its static network architecture. During dynamic evolution iterations, there is a significant exponential increase in the computational costs due to redundant and repetitive computations.In order to address this challenge, this paper introduces a dynamic evolution variant of the Discrete Hopfield Neural Network - Satisfiability model. In extensive simulation experiments, we progressively augmented the number of constraint clauses from 1 to 1500, seeking global minima for CNF problems. The proposed model exhibited congruent performance with the traditional model, achieving a Global Minimum Ratio of 1 and a Hamming distance of 0. Crucially, the proposed model minimized CPU utilization and neared zero error metrics, while the traditional model experienced exponential CPU and error metric escalation. These outcomes affirm the proposed model's robust global search capabilities and high precision, aligning with the traditional model. Furthermore, owing to this model's incorporation of not only temporal constraint increment operator but also innovative real-time learning techniques and clever integration methods, along with the establishment of a novel real-time decision mechanism, the proposed model effectively addresses the issues of redundancy and repeated calculations inherent in traditional models. This results in a stable and significantly improved computational speed. Additionally, this model's dynamic evolution network architecture is specifically designed to accommodate arbitrary and efficient extensions of dynamic constraints, while also featuring a built-in preprocessing function to filter out duplicate variables. In the future, as a dynamic evolution processor, this model exhibits immense potential for solving large-scale SAT problems with dynamic evolution constraints and holds broad application prospects in emerging fields such as automated reasoning, intelligent recommendation systems, and machine learning.

Robots selection for sine trigonometric (l1, l2, l3) neutrosophic sets using different aggregation operators

This article discusses a new approach to multiple attribute decision-making (MADM) based on sine trigonometric (ST) (l1, l2, l3) neutrosophic sets (NS). We discuss the concept of ST (l1, l2, l3) neutrosophic weighted averaging (NWA), ST (l1, l2, l3) neutrosophic weighted geometric (NWG), ST (l1, l2, l3) generalized neutrosophic weighted averaging (GNWA) and ST (l1, l2, l3) generalized neutrosophic weighted geometric (GNWG). We presented during our discussion showed an algorithm that used these operators. Extensive Hamming distances are illustrated numerically. Also included in this communication are discussions of idempotency, boundness, commutativity, and monotonicity for ST (l1, l2, l3) neutrosophic sets. By using them, you can find the best option faster, easier, and more conveniently. As a result, ST (l1, l2, l3) and more precise conclusions are more closely related. A comparison is made between some current models and those proposed to demonstrate the dependability and utility of the current models. Furthermore, fascinating findings were revealed in the study.

Sparse Code with Minimum Hamming Distances of 4 and 5 for Increasing the Density of STT-MRAM Cells

Sparse Code with Minimum Hamming Distances of 4 and 5 for Increasing the Density of STT-MRAM Cells

An Empirical Study on Convergent Teaching of Academic English and General English in the Internet Era

Abstract In this paper, we first use Hamming distance to mine students’ English achievement, construct the data matrix to get the English achievement, and analyze the standard deviation of each row and column in the matrix to get the similarity and the degree of dispersion among the students’ accomplishments. The BGLL algorithm is used to analyze achievement data by clustering. The modularity of the data calculated by clustering is applied to the weighted graph to obtain the results of achievement clustering analysis. By analyzing the effect of the fusion teaching mode of academic English and general English after practice, it was found that the English reading and speaking scores of the students in the experimental class improved significantly. There was a significant difference between the reading posttest scores of the experimental class and those of the control class (p =0.002&lt;0. 05), with a difference of nearly 10 points in the average scores. The student’s awareness score of the importance of English learning after teaching practice reached 4.308, the attitude score of English learning motivation averaged about 3.192, and the student’s knowledge and understanding of English teaching objectives were also more precise, with a mean value of 3.399. The application effect of the integrated English teaching model proposed in this paper is excellent, which lays the foundation for the integrated teaching of academic English and general English, and also provides reference data for the application of the teaching model in the future.

Computer purchasing using new type neutrosophic sets and its extension based on aggregation operators

This article discusses a new approach to multiple attribute decision-making (MADM) based on (l1, l2, l3) neutrosophic sets (NS). This is an extension of the NS. Neuosophic weighted averaging (NWA), neutrosophic weighted geometrics (NWG), generalized neutrosophic weighted averaging (GNWA), and generalized neutrosophic weighted geometrics (GNWG) are the topics of this article. The flowchart we presented during our discussion showed an algorithm that used these operators. Numerical examples are provided for the extended Euclidean and Hamming distance measures. As part of this communication, we will also elaborate on the properties of neutrosophic sets, such as their idempotency, their boundness, their commutativity, and their monotonicity. They make it quicker, easier, and more convenient to find the best option. Thus, there is a stronger connection between (l1, l2, l3) and more precise conclusions. Some of the current models are compared with those that have been proposed in order to demonstrate their dependability and utility. The study also revealed fascinating and intriguing findings.

ОЦЕНКА КАЧЕСТВА МАССОВЫХ ОТКРЫТЫХ ОНЛАЙН-КУРСОВ В ТЕРМИНАХ НЕЧЕТКИХ МНОЖЕСТВ

The article is devoted to the development of a model and algorithm for evaluating the quality of mass open online courses. The algorithm of such an assessment can be used in elearning on MOOC platforms or in elearning management systems for additional professional education or in vocational educational programs of universities. A comparative analysis of existing models, algorithms and methods for evaluating the quality of online courses has been carried out, which indicates a fairly small number of studies in the field of elearning. The choice of quality indicators of mass open online courses is justified, their structure in three dimensions is determined, taking into account the quality of content, course services and student achievements. The quality indicators are systematized, allowing to form a hierarchical graph, on the basis of which an integral indicator of the quality of mass open online courses in terms of fuzzy sets on a threelevel classifier is developed. For an integrated quality assessment, a methodology for assessing the consumer quality of information systems based on fuzzy sets is used. To check the quality of the indicators, the relative Hamming distance between the membership functions is used, which has a trapezoidal shape. The calculation of the quality assessment is given using the example of the online course "Data Analysis in python" in the Moodle elearning management system. To demonstrate the costeffectiveness of online courses, the cost calculation of similar courses in online and offline format was carried out and statistical processing of the results was performed. The purpose of the development is to increase the effectiveness of elearning through the use of a model and an algorithm for evaluating the quality of online courses. This algorithm allows you to determine the quality of a course for a specific MOOC platform or elearning management system and develop an action plan to reduce the number of lowquality online courses. The fuzzy model and algorithm can be integrated into an educational platform to automate the quality assessment of MOOCs and recommend online courses to the student.

Two-Dimensional 15/24 Non-Isolated Patterned Modulation Codes With Minimum Hamming Distance 3 in Bit-Patterned Media Recording

Two-Dimensional 15/24 Non-Isolated Patterned Modulation Codes With Minimum Hamming Distance 3 in Bit-Patterned Media Recording