The COVID-19 outbreak stands as one of the most significant global health crises in recent memory, claiming millions of lives worldwide. The urgent and precise identification of the disease holds paramount importance ...
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Satellite image classification and prediction are crucial, with applications in domains ranging from environmental monitoring to urban planning, geological exploration, mapping, disaster response management, and agric...
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The rampant spread of misinformation undermines the credibility of social media platforms in today's digital landscape [1]. Machine learning techniques have become vital in detecting fake news and maintaining publ...
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Feature extraction is crucial in bioinformatics, as it converts genomic sequences into numerical feature vectors essential for machine learning algorithms, particularly in clustering, to identify the families of newly...
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Feature extraction is crucial in bioinformatics, as it converts genomic sequences into numerical feature vectors essential for machine learning algorithms, particularly in clustering, to identify the families of newly sequenced genomes. Traditional methods have relied on alignment-based techniques for clustering the genomic sequences. However, these methods are computationally intensive. In contrast, alignment-free methods are now more commonly used due to their reduced computational demands. Despite this, many alignment-free approaches may generate identical feature vectors for dissimilar sequences, as they focus solely on single nucleotide counts (1-gram) and their arrangement during feature extraction, often neglecting dinucleotide counts and their arrangement, which can degrade clustering performance. Furthermore, certain approaches include trinucleotide or higher-order compositions;they introduce high-dimensionality issues, resulting in inaccurate results. Additionally, some existing methods are not scalable and take substantial time to extract features from large genomic sequences. To address these issues, we proposed a novel 33-dimensional Scalable Alignment-Free Feature Vector (33d-SAFFV) approach to extract the significantly important features such as length of sequence, count of dinucleotides, and positional sum of dinucleotides, which produces a 33-dimensional feature vector. This approach leverages Apache Spark for scalability and efficient in-memory computations, making it suitable for large datasets. We evaluated the performance of our proposed method by applying the extracted 33-dimensional feature vectors to K-Means and Fuzzy C-Means (FCM) clustering algorithms. Performance is measured using the Silhouette Index (SI) and Calinski-Harabasz (CH) index. Experimental results on the gene sequences of four varieties of rice datasets and two varieties of soybean datasets show the effectiveness of the proposed 33d-SAFFV approach. In K-Means clustering with t
This paper addresses the communication barriers faced by the deaf and voiceless community by introducing a system that translates sign language into spoken or written language. The primary aim is to foster better comm...
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Developing effective strategies to address significant variations in segmented iris image quality is crucial for accurate iris recognition from remotely acquired facial or eye images. These variations are closely link...
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Trustworthy verification of bank checks is crucial for modern financial transaction security and the prevention of fraudulent activities. A novel approach to the authentication and categorization of bank check signatu...
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Because of the growing number of cars and the inadequate parking infrastructure, urban parking management has become more and more crucial. To predict parking spot availability in IoT-enabled intelligent systems, this...
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In cities people often feel out of touch, with nature which leads to awareness and interaction with plants especially when they eat processed foods. Our project aims to change this by creating an app that lets users t...
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A high-speed dynamic comparator with automatic dc offset compensation is proposed in this paper. The comparator is based on a double-tail topology and reduces the dc offset by a calibration circuit. Avoiding using the...
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