Research Article
Exploring Hidden Markov Models in the Context of Genetic Disorders, and Related Conditions: A Systematic Review
Mouhamadou Djima Baranon*,
Patrick Guge Oloo Weke,
Judicael Alladatin,
Boni Maxime Ale,
Amos Kipkorir Langat
Issue:
Volume 13, Issue 4, August 2024
Pages:
69-82
Received:
20 April 2024
Accepted:
6 May 2024
Published:
5 July 2024
Abstract: The application of Hidden Markov Models (HMMs) in the study of genetic and neurological disorders has shown significant potential in advancing our understanding and treatment of these conditions. This review assesses 77 papers selected from a pool of 1,105 records to evaluate the use of HMMs in disease research. After the exclusion of duplicate and irrelevant records, the papers were analyzed for their focus on HMM applications and regional representation. A notable deficiency was identified in research across regions such as Africa, South America, and Oceania, emphasizing the need for more diverse and inclusive studies in these areas. Additionally, many studies did not adequately address the role of genetic mutations in the onset and progression of these diseases, revealing a critical research gap that warrants further investigation. Future research efforts should prioritize the examination of mutations to deepen our understanding of how these changes impact the development and progression of genetic and neurological disorders. By addressing these gaps, the scientific community can facilitate the development of more effective and personalized treatments, ultimately enhancing health outcomes on a global scale. Overall, this review highlights the importance of HMMs in this area of research and underscores the necessity of broadening the scope of future studies to include a wider variety of geographical regions and a more comprehensive investigation of genetic mutations.
Abstract: The application of Hidden Markov Models (HMMs) in the study of genetic and neurological disorders has shown significant potential in advancing our understanding and treatment of these conditions. This review assesses 77 papers selected from a pool of 1,105 records to evaluate the use of HMMs in disease research. After the exclusion of duplicate and...
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Research Article
Novel Integer Division for Embedded Systems: Generic Algorithm Optimal for Large Divisors
Mervat Mohamed Adel Mahmoud*,
Nahla Elazab Elashker
Issue:
Volume 13, Issue 4, August 2024
Pages:
83-93
Received:
9 June 2024
Accepted:
1 July 2024
Published:
24 July 2024
Abstract: The integer Constant Division (ICD) is the type of integer division in which the divisor is known in advance, enabling pre-computing operations to be included. Therefore, it can be more efficient regarding computing resources and time. However, most ICD techniques are restricted by a few values or narrow boundaries for the divisor. On the other hand, the main approaches of the division algorithms, where the divisor is variable, are digit-by-digit and convergence methods. The first techniques are simple and have less sophisticated conversion logic for the quotient but also have the problem of taking significantly long latency. On the contrary, the convergence techniques rely on multiplication rather than subtraction. They estimate the quotient of division providing the quotient with minimal latency at the expense of precision. This article suggests a precise, generic, and novel integer division algorithm based on sequential recursion with fewer iterations. The suggested methodology relies on extracting the division results for non-powers-of-two divisors from those for the closest power-of-two divisors, which are obtained simply using the right bit shifting. To the authors’ best knowledge of the state-of-the-art, the number of iterations in the recurrent variable division is half the divisor bit size, and the Sweeney, Robertson, and Tocher (SRT) division, which is named after its developers, involves log2(n) iterations. The suggested algorithm has an [(m/(n-1))-1] number of recursive iterations, where m and n are the number of bits of the dividend and the divisor, respectively. The design is simulated in the Vivado tool for validation and implemented with a Zynq UltraScale FPGA. The technique performance depends on the number of nested divisions and the size of a LUT. The two factors change according to the value of the divisor. Nevertheless, the size of the LUT is proportional to the range and the number of bits of the divisor. Furthermore, the equation that controls the number of nested blocks is illustrated in the manuscript. The proposed technique applies to both constant and variable divisors with a compact hardware area in the case of constant division. The hardware implementation of constant division has unlimited values for dividends and divisors with a compact hardware area in the case of large divisors. However, using the design in the hardware implementation of variable division is up to 64-bit dividend and 12-bit divisor. The result analysis demonstrates that this algorithm is more efficient for constant division for large numbers.
Abstract: The integer Constant Division (ICD) is the type of integer division in which the divisor is known in advance, enabling pre-computing operations to be included. Therefore, it can be more efficient regarding computing resources and time. However, most ICD techniques are restricted by a few values or narrow boundaries for the divisor. On the other han...
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