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Investigation of DNA discontinuity for detecting tuberculosis

Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1149-1163页

作者： Nimmy, Sonia Farhana Sarowar, Md. Golam Dey, Nilanjan Ashour, Amira S. Santosh, K.C. Department of Computer Science and Engineering Notre Dame University Dhaka Bangladesh Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Information Technology Techno India College of Technology Kolkata India Department of Electronics and Electrical Communications Engineering Faculty of Engineering Tanta University Tanta Egypt Vermillion United States

Discontinuity in long Deoxyribonucleic Acid (DNA) sequences creates harmful diseases. Changes in the DNA structure refers to changes in the human immunity system. Tuberculosis is a critical disease that causes coughing, fatigue, unintentional weight loss and fever on aged people due to the disorder in the DNA. Breaks or mutations over long DNA sequences are the pivotal reasons for this fatal disease. This study developed an automated machine learning technique to assess the total number of such breaks in the long DNA sequences. Data cleansing and deep neural network techniques are applied to handle this big data. The National Center for Biotechnology Information (NCBI) database has been used to extract the amino acid sequences for Tuberculosis disease from the big DNA datasets. Results reveal that the proposed automated approach is significantly effective for the determination of DNA sequence breaks for the tuberculosis diseases due to the high sensitivity of Markov chain as well as the effective normalization techniques. This approach fixed the size of the training datasets and recursively divide the whole dataset into certain length. The study also adopts multiple predictions approaches, such as the hidden Markov chain, Box-Cox transformation and linear transformation to forecast about the breaks for any long positions of the training and testing datasets. The results demonstrated that hidden the Markov chain model provided faster analysis with more accurate and reliable results. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： DNA

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A System for Constructing Spanning Trees in Graph Networks that Utilize Integer Linear Programming to Enhance Link Fault Tolerances 14

A System for Constructing Spanning Trees in Graph Networks t...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： The, Hieu Tran Fujita, Kosuke Kitsuwan, Nattapong The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798350313277

In this paper, we formulate to optimize the problem related to network failure using the integer linear programming (ILP) method. We aim to minimize the number of spanning trees needed to protect the network in case of link and/or link-node failure. Compared to the traditional approach of constructing spanning trees using heuristic algorithms, our method has successfully reduced the number of trees required for node or link-node failure protection by up to 50%. Reducing the number of spanning trees saves memory requirements for the network route and simplifies network configuration. © 2023 IEEE.

关键词： integer linear programming link failure Network protection reducing memory spanning trees

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Fitness Function Using Postconditions for Automated Program Repair 3

Fitness Function Using Postconditions for Automated Program ...

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3rd IEEE International Conference on Software engineering and Artificial Intelligence, SEAI 2023

作者： Ito, Yusaku Washizaki, Hironori Fukazawa, Yoshiaki Waseda University Department of Computer Science and Communications Engineering Tokyo Japan

ISBN: (纸本)9798350337952

Genetic algorithm-based automated program repair has the challenge that execution time tends to be extremely long. One of the causes is the low adequacy of the fitness, which represents the evaluation of program variants. To improve the efficiency of automated repair, we propose a new fitness function that uses the state of internal variables of the program in addition to the results of test runs. First, candidate specifications are randomly generated using variables in the program. Next, the correct specification is automatically estimated from the candidates, thus making the granularity of the fitness function more fine-grained. Experimental results with small programs show that the proposed method extracts the postcondition of the program appropriately in most cases. On the other hand, experiments with the Defects4J Math dataset suggest that the proposed method does not necessarily improve the efficiency of automated repair and may reduce the diversity of generated variants. © 2023 IEEE.

关键词： Genetic algorithms

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Spectrum Slicer Placement Optimization by Betweenness Centrality in Elastic Optical Network 14

Spectrum Slicer Placement Optimization by Betweenness Centra...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Hachiya, Tsubasa Kitsuwan, Nattapong The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798350313277

This paper proposes a more effective placement way of slicers to increase bandwidth efficiency in elastic optical network (EON). EON enhances the potential of the network by dividing available bandwidth into a small region called "slot"and assigns bandwidth to communication requests dynamically. Spectrum slicing also improves the availability of bandwidth. However, these technologies cause bandwidth waste due to the lost opportunities of spectrum slicing with the incomplete placement of slicers. We provide more optimal placement of spectrum slicers to advance bandwidth efficiency. Betweenness centrality (BC) shows the importance of nodes in sight of network routing. It allows us to predict which node will be crowded and plan better slicer installation assignments in advance. We simulate how effective the proposed method is to reduce communication conflicts with the index allocation blocking ratio. As a result of simulations, the proposed method reduces communication conflicts by up to 91.01% compared to a previous BC method. © 2023 IEEE.

关键词： Elastic optical network optimization spectrum slicing

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A Machine Learning Approach to Customer Complaint Handling: Ensemble Classification and Escalation 15

A Machine Learning Approach to Customer Complaint Handling: ...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kruthi, S. Sinha, Shal Ritvik Adarsh Nayak, S.R. Jamadagni, Suresh Gaurav, U. PES University Department of Computer Science and Engineering Bengaluru India PES University Department of Electronics and Communications Engineering Bengaluru India

ISBN: (纸本)9798331300579

This paper presents a novel approach to revolutionize customer complaint handling through a machine learning-based ensemble classification system and an automated escalation mechanism. The research involves the development of an ensemble model, integrating random forest, XGBoost, and Support Vector Machine (SVM), selected from a thorough evaluation of 10 machine learning models. The objective is to streamline the classification of customer emails into predefined categories-specifically, "replacements and refunds issues", "account related issues", and "others". The methodology involves the extraction and manual labelling of approximately 2000 emails that were obtained after web scraping. The email bot incorporates an algorithm to determine urgency levels based on factors such as product cost, complaint history, product importance, customer sentiment, and keyword analysis. Integrated with the Gmail API, the bot efficiently tags and escalates incoming emails to the relevant department. Performance metrics, including total resolution time and write cycles, gauge the success of the implemented solution. Results indicate a significant reduction in the time and effort required for complaint resolution, thereby enhancing overall customer experience. This research emphasizes the advantages of leveraging machine learning to optimize customer support processes, paving the way for future advancements in this field. © Grenze Scientific Society, 2024.

关键词： Decision trees

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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Cache Node Placement for Maximum Traffic Minimization in Content-Centric Networking 14

Cache Node Placement for Maximum Traffic Minimization in Con...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Nakajima, Shohei Kitsuwan, Nattapong The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9798350313277

Content-centric networking (CCN) is a network architecture based on content. The conventional network architecture uses IP address to route packets based on location. In CCN, which routes packets based on content name, a cache function can be attached to nodes. Getting contents from cache nodes alleviates traffic congestion in the network. In the case that the cache function is not attached to all nodes in the network, it is necessary to decide where cache nodes are placed. Congestion tends to occur around cache nodes when using the conventional method. We formulate a problem of cache node placement as an integer linear programming (ILP) considering the population of each node. The ILP formulation determines the cache node placement to minimize the value of the maximum traffic. This result shows the proposed ILP approach reduces the maximum traffic by 59% on average. © 2023 IEEE.

关键词： Content-centric networking maximum traffic optimization

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Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos

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computer Systems Science & engineering 2023年第8期46卷 2489-2508页

作者： MD.Yasar Arafath A.Niranjil Kumar Department of Computer Science Engineering Jairupaa College of Engineering ThottiapalayamTirupur District641604TamilnaduIndia Department of Electronics and Communications Engineering Dhanalakshmi College of EngineeringChennai600015TamilnaduIndia

For intelligent surveillance videos,anomaly detection is extremely *** learning algorithms have been popular for evaluating realtime surveillance recordings,like traffic accidents,and criminal or unlawful incidents such as suicide ***,Deep learning methods for classification,like convolutional neural networks,necessitate a lot of computing *** computing is a branch of technology that solves abnormal and complex problems using quantum *** a result,the focus of this research is on developing a hybrid quantum computing model which is based on deep *** research develops a Quantum Computing-based Convolutional Neural Network(QC-CNN)to extract features and classify anomalies from surveillance footage.A Quantum-based Circuit,such as the real amplitude circuit,is utilized to improve the performance of the *** far as my research,this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance *** are 13 anomalies classified from the UCF-crime *** on experimental results,the proposed model is capable of efficiently classifying data concerning confusion matrix,Receiver Operating Characteristic(ROC),accuracy,Area Under Curve(AUC),precision,recall as well as *** proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37%greater when compared to other existing *** measure the efficiency of the proposed work,QC-CNN is also evaluated with classical and quantum models.

关键词： Deep learning video surveillance quantum computing anomaly detection convolutional neural network

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Compensated Stepped Microstrip Lines: A Novel Approach for Impedance Discontinuity Mitigation 11

Compensated Stepped Microstrip Lines: A Novel Approach for I...

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11th IEEE Open Conference of Electrical, Electronic and Information Sciences, eStream 2024

作者： Vasianov, Aleksandr Barzdenas, Vaidotas Wang, Sen Department of Computer Science and Communications Technologies Vilnius10105 Lithuania Department of Electronic Engineering Taipei10608 Taiwan

ISBN: (纸本)9798350352412

Modern electronic devices operate at frequencies where interconnects function as transmission lines, necessitating meticulous management of characteristic impedance (Z0) to avert signal degradation and power losses. This paper delves into the manipulation of microstrip characteristic impedance through variations in trace inductance and capacitance. It proposes a reference plane cut-out compensation technique to mitigate impedance inconsistencies introduced by component pads of varied sizes, which disrupt the uniformity of the microstrip. The technique involves adjusting capacitance by altering the distance to the reference plane. Experimental validation on a 6-layer printed circuit board (PCB) device confirms the effectiveness of the proposed method in maintaining impedance close to the target value of $50\Omega$, critical for high-frequency applications. Measurements using a vector network analyzer demonstrate substantial impedance improvement up to $35\Omega$ through compensation, offering insights into practical impedance control strategies for high-frequency dense PCB designs. © 2024 IEEE.

关键词： Capacitance

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A Scalable Deep Learning Framework for Dynamic CSI Feedback With Variable Antenna Port Numbers

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IEEE Transactions on Wireless communications 2024年第4期23卷 3102-3116页

作者： Lin, Yu-Chien Lee, Ta-Sung Ding, Zhi University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu30010 Taiwan

Transmitter-side channel state information (CSI) is vital for large MIMO downlink systems to achieve high spectrum and energy efficiency. Existing deep learning architectures for downlink CSI feedback and recovery show promising improvement of UE feedback efficiency and eNB/gNB CSI recovery accuracy. One notable weakness of current deep learning architectures lies in their rigidity when customized and trained according to a preset number of antenna ports for a given compression ratio. To develop flexible learning models for different antenna port numbers and compression levels, this work proposes a novel scalable deep learning framework that accommodates different numbers of antenna ports and achieves dynamic feedback compression. It further reduces computation and memory complexity by allowing UEs to feedback segmented DL CSI. We showcase a multi-rate successive convolution encoder with under 500 parameters. Furthermore, based on the multi-rate architecture, we propose to optimize feedback efficiency by selecting segment-dependent compression levels. Test results demonstrate superior performance, good scalability, and high efficiency for both indoor and outdoor channels. © 2002-2012 IEEE.

关键词： Scalability

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