Plant classification is a critical task with many practical applications, including agriculture, environmental management, and biodiversity conservation. Traditional methods of plant classification can be time-consumi...
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This study applies single-valued neutrosophic sets, which extend the frameworks of fuzzy and intuitionistic fuzzy sets, to graph theory. We introduce a new category of graphs called Single-Valued Heptapartitioned Neut...
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Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics incl...
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Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a
The enormous variations in food choices and lifestyle in today’s world have given rise to the demand of using recommender system as a suitable tool in making appropriate food choices. Need for choosing nutritious foo...
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Age-related Macular Degeneration (AMD) is a leading cause of visual impairment among the elderly worldwide. This study compares deep learning-based and classical feature extraction methods for AMD classification using...
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Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and *** resources are vital for all countries in terms of their economies and *** a result,selecting the optimal o...
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Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and *** resources are vital for all countries in terms of their economies and *** a result,selecting the optimal option for any country is critical in terms of energy *** country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global *** the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous *** study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp *** hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective *** best-suited renewable energy alternative is discovered using the approach presented.
Depression is a major public health concern, affecting millions worldwide, and necessitates early, accurate detection for timely intervention. This study focuses on enhancing machine learning (ML) and deep learning (D...
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Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estima...
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Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.
The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks....
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The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text *** methods typically use simplified molecular input line entry system(SMILES)to repre...
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The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text *** methods typically use simplified molecular input line entry system(SMILES)to represent molecules and rely on diffusion models or autoregressive structures for ***,the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance,which affects the efficiency of model training and *** this paper,we propose a text-guided diverse-expression diffusion(TGDD)model for molecule *** combines both SMILES and self-referencing embedded strings(SELFIES)into a novel diverse-expression molecular representation,enabling precise molecule mapping based on natural *** leveraging this diverse-expression representation,TGDD simplifies the segmented diffusion generation process,achieving faster training and reduced memory consumption,while also exhibiting stronger alignment with natural *** outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.
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