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Negative Itemset Tree for Discovering Rare Patterns with Periodicity from Static Databases

engineering Letters 2023年第3期31卷 882-895页

作者： Jyothi Upadhya, K. Bolisetty, Thanishka Rao, B. Dinesh Geetha, M. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Manipal School of Information Sciences Manipal Academy of Higher Education Karnataka Manipal576104 India

Periodic patterns assist in the discovery of crucial information in the fields, including fraud detection, telecommu-nications, retail marketing, research, and medicine. Numerous methods have been developed for the extraction of periodic frequent patterns. The capacity to find uncommon or unexpected combinations that are missed by periodic frequent pattern mining techniques is the main motivation for Periodic Rare Pattern Mining. If the rare patterns are distributed across the transaction dataset, they are periodic and important. A novel PRPNegTidTreeMiner algorithm is proposed to discover rare patterns with periodicity along with support threshold. An efficient tree structure, NegTidTree, is constructed to capture the complete negative representation of the given static database. NegTidTree, serves the dual purpose of finding support count as well as periodicity information. This tree accelerates the extraction of periodic rare patterns and prevents repeated database scanning. PRPNegTidTreeMiner uses two mining techniques to generate periodic rare patterns. The first mining method, NegTidTreeMiner, employs a top-down approach to find rare patterns with periodicity thresholds which avoids traversal of frequent patterns. In the second technique, NegTidTreeMiner-FLP, the mining efficiency is improvised by avoiding the traver-sal of frequent as well as non-existing patterns. Experiments are carried out by varying support and periodicity components for a variety of datasets. The results show that NegTidTreeMiner performs better than NegTidTreeMinerFLP when the dataset is small and generates a huge number of periodic rare patterns. When the size of the database grows, NegTidTreeMinerFLP continuously outperforms NegTidTreeMiner. © 2023, International Association of Engineers. All rights reserved.

关键词： Database systems

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Accident Prediction Using KNN Algorithm 4

Accident Prediction Using KNN Algorithm

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4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Akhiladevi, M. Anitha, K. Amrutha, K. Amrutha, M. Chandanashree, K.R. Global Academy of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

Today, one of the top priorities of governments istrafficsafety. Given the importance of the subject, identifying the causes of road accidents has become the primary goal in reducing the damage caused by traffic accidents. Machine learning and data mining concepts are usedto recognize the various factors that influence road accidents andtheir severity. The application uses at odd Machine Learning algorithms which includes K-Nearest-Neighbor, Decision tree, Random forest, etc make predictions based on various parameters, within allthese models KNN gives the best accuracy. This informationcan be used to analyze future inputs and improve the system'soutput accuracy. This model can be improved further to send the accident report to the appropriate authorities, such as hospitals, ambulances, and insurance companies, and can thus be very useful in reducing accident fatality rates in the country. © 2022 IEEE.

关键词： Roads and streets

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Research on Spatio-Temporal Data Processing Framework 21

Research on Spatio-Temporal Data Processing Framework

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： He, Liu Ao, Ren Jianping, Li College of Computer Science Chongqing University Chongqing401331 China Chongqing Academy of Educational Sciences Chongqing400015 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Aiming at the challenges in time series data processing, this paper proposes a new deep learning model, which uses Spiking neurons with time series processing ability to organize, represent and transmit information instead of traditional methods. By combining the time representation characteristics of Spiking neurons with the hierarchical deep connection advantages of complex networks, this study explores a deep learning model combining time and space, which shows significant advantages in the representation and processing ability of time series data. Specifically, we design and implement a model that combines the Spiking mechanism with complex networks. This model can effectively capture the dynamic changes in time series data and improve processing efficiency. The experimental results show that the proposed model has achieved good performance on multiple time series data sets, which verifies its effectiveness and feasibility in time series data representation and processing. This study provides a new method for the field of time series data analysis, which is expected to be widely used in intelligent systems, financial forecasting, bioinformatics and other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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Spiking High-Dimensional Information Coding Based on Wavelet Decomposition Technique 21

Spiking High-Dimensional Information Coding Based on Wavelet...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

ISBN: (纸本)9798331519254

This paper studies the Spiking high-dimensional information coding method based on wavelet decomposition technology. Firstly, the wavelet decomposition technique is used to extract the multi-scale features of the input signal to capture the time-frequency characteristics of the signal. Subsequently, the extracted features are mapped to the pulse sequence of the Spiking neural network to achieve efficient coding of highdimensional information. In this paper, the influence of wavelet decomposition parameters on coding performance is analyzed, and the advantages of the proposed method in processing highdimensional data are verified by experiments. The experimental results show that the proposed method can significantly reduce the data dimension and computational complexity while maintaining the accuracy of information, which provides a new effective way for high-dimensional information processing. © 2024 IEEE.

关键词： Network coding

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Design and Implementation of Advanced Driver Assistance Systems

Design and Implementation of Advanced Driver Assistance Syst...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ali, Waleed Abd-Elshafi Saleh, Hatem Abdelhamid Eid, Haneen Salah Fathi, Ahmed Mohamed Mohamed, Abd-El Rahman Amr Higher Institute of Engineering EL- Shorouk Academy Department of Communications and Computer Engineering Cairo Egypt Misr University for Science and Technology Department of Electronics and Communications Engineering Giza Egypt

ISBN: (纸本)9798350351408

Advanced Driver Assistance Systems (ADAS) are a pivotal advancement in automotive technology, enhancing vehicle safety and driving experience. In our project, we have successfully designed and built a robotic prototype car. We have implemented a comprehensive control system for the car's movement, and the following features of ADAS systems: Bump Detection, Precise Lane Tracking, Adaptive Cruise Control, Traffic Sign Recognition, and Blind Spot Detection. Bump detection systems are equipped to detect and alert drivers of speed bumps and other road irregularities, in which we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different Bumps on the road and we use Python, TensorRT, and OpenCV. Lane Departure Warning (LDW) alerts drivers when their vehicle unintentionally drifts out of their lane, we use NVIDIA Jetson Nano as our main microcontroller unit (MCU) responsible for our Machine learning and Astra Pro Plus Depth camera as our RGB sensor, we use Python, Python's libraries such as OpenCV, and NumPy to implement LDW. Adaptive Cruise Control (ACC) adjusts the vehicle's speed automatically to maintain a safe distance from the vehicle ahead, we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different vehicles on the road, we use Python, TensorRT, and OpenCV. Traffic Sign Recognition can interpret and display traffic signs to keep drivers informed about speed limits, no-entry signs, and more, we use NVIDIA Jetson Nano, and Astra Pro Plus Depth camera as our RGB sensor and use Custom AI Model based on YOLOv5 to detect different Traffic Signs on the road, we use Python, TensorRT, and OpenCV. Blind Spot Detection (BSD) technology helps drivers identify vehicles in their blind spots and alerts them to avoid dangerous maneuvers, we use ESP32 as an MCU and Ultrasonic sensor, and use C++ to program the BSD feature. © 2024

关键词： Advanced driver assistance systems

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RISC-V Multicore for Miniature DNA Sequencers

RISC-V Multicore for Miniature DNA Sequencers

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Beyene, Abel Wu, ZhongPan Hammad, Karim Ghafar-Zadeh, Ebrahim Magierowski, Sebastian York University Electrical Engineering and Computer Science Toronto Canada Technology and Maritime Transport Arab Academy for Science Cairo Egypt

ISBN: (纸本)9798350300260

Hand-sized DNA sequencers have demonstrated excellent scientific utility. They also present exciting opportunities for the expansion of DNA analysis to a diverse range of uses and users. But this potential is currently blunted by mini-sequencers' lack of on-board computing. In this paper we explore the potential of embedded processors to address this gap. We outline the general hardware and software organization of existing systems and identify a deep learning bioinformatics task as an exemplary workload for processors embedded in miniature sequencers. We then describe a candidate multicore RISC-V arrangement and experimentally study the relative performance, power, and cost trade-offs for alternate configurations of this arrangement. The experiments show no saturation in performance scaling up to at least 6 cores with maximum throughput and efficiency achieved with 512KB and 128KB L2 cache sizes respectively. © 2023 IEEE.

关键词： DNA sequencing embedded computing machine learning multicore RISC-V

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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing

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Light(science & Applications) 2025年第1期14卷 227-237页

作者： Xiangyan Meng Nuannuan Shi Guojie Zhang Junshen Li Ye Jin Shiyou Sun Yichen Shen Wei Li Ninghua Zhu Ming Li Key Laboratory of Optoelectronic Materials and Devices Institute of SemiconductorsChinese Academy of SciencesBeijing100083China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of SciencesBeijing100049China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing100049China China Academy of Space Technology(Xi’an) Xi’anShaanxi710100China WeChat Pay Lab 33 Shenzhen Tencent Computer System Co.Ltd.Shenzhen518054China Lightelligence Group Hangzhou311121China

The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing *** from the available optical multidimensional information entropy,optical intelligent computing is used as an alternative solution to address the emerging challenges of electrical *** limitations,in terms of device size and photonic integration scale,have hindered the performance of optical ***,an ultrahigh computing density optical tensor processing unit(OTPU),which is grounded in an individual microring resonator(MRR),is introduced to respond to these *** the independent tuning of multiwavelength lasers,the operational capabilities of an MRR are orchestrated,culminating in the formation of an optical tensor *** design facilitates the execution of tensor convolution operations via the lightwave and microwave multidomain hybrid multiplexing in terms of the time,wavelength,and frequency of *** experimental results for the MRR-based OTPU show an extraordinary computing density of 34.04 TOPS/***,the achieved accuracy rate in recognizing MNIST handwritten digits was 96.41%.These outcomes signify a significant advancement toward the realization of high-performance optical tensor processing chips.

关键词： tuning resonator tensor

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Fortaleza: The Emergence of a Network Hub

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arXiv 2023年第1期3卷 272-279页

作者： Bragion, Eric Akter, Habiba Kumar, Mohit Xu, Minxian Abdelmoniem, Ahmed M. Gill, Sukhpal Singh School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Jalandhar India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. © 2023, CC0.

关键词： Cloud data centre Latency Network hubs Fortaleza Roubaix Networking

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Pendant Graphs: Unveiling Regularity, Irregularity, and Support Vertices

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IAENG International Journal of Applied Mathematics 2025年第1期55卷 45-51页

作者： Mathew, Suji Elizabeth Kalayathankal, Sunny Joseph Thankachan, Baiju Mathew, Bibin Department of Mathematics Catholicate College Kerala Pathanamthitta689643 India Department of Computer Science and Engineering Rajagiri School of Engineering and Technology Kerala Kochi682039 India Department of Mathematics Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Education National Institute of Technology Calicut Kerala 673601 India

This paper investigates Pendant Graphs, which are graphs that contain pendant vertices. It introduces a new perspective on the regularity and irregularity of pendant graphs, and discusses their relationship with Highly Irregular Graphs. Additionally, this work introduces a novel graph based on the concept derived from (m, k) Regular Graphs. The (m, k) regular graphs are closely related to Pendant Graph. The paper also delves into the nature and characteristics of pendant vertices. By exploring the characteristics of pendant vertices and defining the Cyclic Dendrimer Graph, the paper significantly contributes to both Graph Theory and Chemical Graph Theory. © (2025), (International Association of Engineers). All rights reserved.

关键词： Graph theory

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An Enhanced Approach to Intelligent computer-Assisted Localization of Liver Tumor on Computed Tomography Images

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SN computer science 2024年第7期5卷 1-10页

作者： Arakeri, Megha Lakshmana Reddy, Raghavendra Ravishankar, H. Deepa, K.R. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India Seamovations Labs Pvt Ltd Karnataka Bengaluru India School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computer Science and Information Technology REVA University Karnataka Bengaluru India School of Electrical and Electronics Engineering REVA University Karnataka Bengaluru India

Computed Tomography (CT) Imaging is frequently used to find liver cancer. CT imaging of the liver generates cross-sectional images of the abdominal region. The task of segmenting a liver tumor on CT images is tedious due to the anatomic complexity of the liver. Therefore, liver tumor detection has been performed manually or semi-automatically which is an exhaustive process. Further, a tumor may be present on only some slices of the CT scan and hence there is a need to automatically identify images that contain tumors from acquired CT images and then locate a region of the tumor on those images for further diagnosis. Automatic detection helps the radiologist to obtain fast, accurate, and effective results. Accordingly, in this paper, an enhanced approach is proposed based on deep learning, efficient fuzzy c-means (EFCM) clustering, and a region-based algorithm for the detection of a liver tumor. A deep learning model built on a convolutional neural network is used to identify whether the CT image contains a tumor. If it contains the tumor, then the proposed EFCM and region-based algorithms are applied to locate the tumor on the CT image. The EFCM algorithm is combined with a region-based technique to automatically determine the initial pixel and threshold values required to segment the liver tumor. The proposed method is applied to several CT abdominal images. The results of the tumor segmentation are validated by comparing them with the tumor regions detected by radiologists. The estimated results of the experiment show that the proposed technique successfully detects the liver tumor on the CT image with less than 2% relative error. © The Author(s) 2024.

关键词： Computed tomography computer-aided detection Healthcare Liver tumor Medical image Segmentation

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