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AMPP: An Adaptive Multilayer Perceptron Prefetcher for Irregular data Prefetching

AMPP: An Adaptive Multilayer Perceptron Prefetcher for Irreg...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Juan Fang Jingjing Li Huijing Yang Yuening Wang Shuying Song Faculty of Information Technology Beijing University of Technology Beijing China

With the continuous increase in modern data scale, there is a higher demand for processor computing power for time-consuming and computationally intensive data mining problems. High performance computing (HPC) can enable faster processing of large datasets and complex algorithms. data prefetching is an effective technique used in HPC. It can hide memory delay and boost execution performance by obtaining data before the program needs it. Traditional data prefetchers show poor performance on programs with irregular access patterns due to the complex access patterns in its workload is difficult to predict, and may lead to serious cache miss. Applying machine learning algorithm into data prefetching policy for access prediction can effectively improve system performance. In this paper, we propose a machine learning-based prefetcher called Adaptive Multilayer Perceptron Prefetcher (AMPP), combined with a cosine annealing learning rate scheduler. AMPP regards prefetching as a classification problem, it analyzes the given instruction pointer using a multilayer perceptron model and can use the learning rate scheduler to optimize the model during the training process. We apply AMPP on last level cache and use ChampSim simulator to conduct simulation experiments based on GAP and SPEC CPU benchmark suits. Our experiments demonstrate that AMPP provides average IPC improvement of 35.58%, MPKI improvement of 46.08% and coverage of 51.32% compared to no-prefetching baseline. And it performs especially well on benchmarks with irregular data access patterns.

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Using data Analysis Methods for Predicting the Concentration of Toxic Elements in Soil

Using Data Analysis Methods for Predicting the Concentration...

引用

IEEE international Workshop on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications

作者： Lyazat Naizabayeva Gulnara Zakirova International Information Technology University Almaty Kazakhstan

This article is aimed at the selection of machine learning algorithms for the perception of elements in the composition of the soil. In our study, machine learning aims to track changes in the toxicity of contaminated soils in order to predict the level of human health hazard of a contaminated soil condition. For this, the following tasks were set: 1) collection and analysis of data on the types of soil toxicity; 2) study of a mathematical model of toxicity to assess the level of resistance; 3) testing the concentration of toxic elements in soils using four machine learning algorithms. Four machine learning algorithms were tested in the article to predict the content of heavy metals in the soil. The results show that the use of machine learning algorithms allows achieving a high degree of prediction of harmful elements concentrations in soils, which can be useful for making decisions on managing pollution sites. Further improvement of data accuracy and selection of the model and algorithm can also increase the accuracy of prediction. As a result of forecasting using the K-Nearest Neighbors algorithm, the accuracy of the model was achieved at the level of 68%, and using the Decision Tree - 69%. The results of the experiment make it possible to predict the measures of danger to human health from the state of contaminated soil. This will help reduce the risks of the relationship between soil pollution and human health. The obtained results can be useful for specialists in environmental protection, land resource management, and decision-making.

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Machine Learning and Deep Learning algorithms for Network data Analytics Function in 5G Cellular Networks

Machine Learning and Deep Learning Algorithms for Network Da...

引用

international conference on Inventive Computation Technologies (ICICT)

作者： Ale Pavani A. Kathirvel Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

Machine learning and deep learning algorithms have the potential to revolutionize network data analytics in 5G cellular networks. With the increase in the number of connected devices and the explosion of data generated by these devices, traditional methods of network data analytics are becoming increasingly inadequate. However, this function faces several challenges, including managing massive amounts of data, processing data in real-time, addressing privacy concerns, integrating with other network systems, and requiring skilled professionals. Overcoming these challenges will enable the network data analytics function to achieve its objectives and improve the overall performance and reliability of 5G cellular networks. Machine learning and deep learning algorithms enable the automatic identification of patterns and insights in large volumes of data, thereby facilitating real-time decision-making and proactive network management. These algorithms can be used for a wide range of network data analytics functions, including network optimization, anomaly detection, prediction of network failures, and dynamic spectrum management. Deep learning algorithms have shown significant promise in processing unstructured network data such as network logs, video, and images. As the 5G network continues to grow and evolve, the use of machine learning and deep learning algorithms in network data analytics is likely to become increasingly essential for ensuring network efficiency, reliability, and security.

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Developing a Pre-Consultation System using Machine Learning for Medical Diagnostics

Developing a Pre-Consultation System using Machine Learning ...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Medisetty Likhitha G Kalyani Tulluri Naga Vennela Dharmapuri Mahith Paul Department of Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

Due to their lifestyle choices and environment, people nowadays suffer from a broad variety of ailments. Therefore, it becomes crucial to explore the potential for earlier disease prediction. However, doctors find it impossible to accurately forecast based solely on symptoms. The most difficult challenge is making an accurate disease forecast. Machine learning is crucial in disease prediction to address this problem. In the area of medicine, there is a huge data increase each year. As a result of the expansion of data in the medical and healthcare sectors, reliable analysis of medical data—which benefited from early patient care—has grown. Machine learning algorithms can discover possibilities in the provided medical dataset with the use of disease data. The main objective of this study is detecting the probable disease with high accuracy and challenges are data quality & quantity and high dimensionality. This study has suggested a broad possibility of disease occurrence based on the symptoms of the patient. Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naive Bayes algorithms are used to accurately forecast diseases. A collection of disease symptoms is necessary for disease *** accuracy of the prediction is the main challenge which depends on the quality and quantity of the data used to train the model. Medical data can be incomplete, biased, or inconsistent, which can affect the performance of the model. So our goal is to develop the correct accuracy prediction model.

关键词： Machine learning algorithms Predictive models Prediction algorithms data models Decision trees Reliability Prognostics and health management

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Integration of Big data Analysis and Computation on the Assessment of User Experience in Cultural Curation Based on Co-occurrence Network of Google Search in the 2023 Taiwan Hakka Expo

Integration of Big Data Analysis and Computation on the Asse...

引用

Electronic Communications, Internet of Things and Big data (ICEIB), international conference on

作者： Shengming Wang Yuanchen Zhang Chuding Chen Department of Design National Taipei University of Technology Taipei Taiwan Doctoral Program in Design National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

By using big data, we evaluated the user experience (UX) of cultural curation for a case study of the 2023 Hakka Expo in Taiwan (2023 HEIT). Attendees experienced a 360-degree virtual recreation of the curation, facilitated by advanced computing and processing technologies, including hardware and software that supported the immersive experience. They were interviewed to recall and compare their physical and digital UX. Responses to the User Experience Questionnaire (UEQ) were included in the quantitative big dataset. The data included interview transcripts, news articles, curatorial design proposals, and other documentation relating to the 2023 HEIT, all of which were processed using sophisticated software for text analysis. The data analysis results revealed users rated the pragmatic and hedonic aspects of 2023 HEIT on the UEQ. However, text analysis on the thick data was used to identify 11 high-frequency themes/concepts designed by curators, and only 4 aligned with the data sources. Specifically, the user interview results surfaced only 4 of these 11 core curatorial concepts from official materials. Key elements were not fully conveyed to users. This mixed-methods approach highlighted the complementary value of integrating big and thick data, along with advanced computing and processing tools, for nuanced UX evaluation. Future research is necessary to explore synergies between quantitative and qualitative analysis in assessing cultural curation and experiences.

关键词： Surveys Text analysis data analysis Big data User experience Cultural differences Interviews

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Strengthening Cloud data Security using Web- Based File Encryption and Decryption with AESCBC- 256

Strengthening Cloud Data Security using Web- Based File Encr...

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Integrated Intelligence and Communication Systems (ICIICS), international conference on

作者： Pachipala Yellamma Rohitha Papolu Nethri Pendem Pujitha Karanam Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India

Ensuring robust data security in cloud computing remains a pressing concern, as the proliferation of cloud services introduces vulnerabilities and risks. This research paper focuses on the implementation of web-based encryption and decryption mechanism for files stored in cloud computing environments, employing the AES-CBC-256 encryption algorithm to rectify the backlogs in cloud security. The encryption algorithm that is extensively employed in the realm of computer security and cryptography is AES-CBC-256, which stands for Advanced Encryption Standard with Cipher Block Chaining mode and a key size of 256 bits. Its primary purpose is to provide secure and confidential communication and data protection. When comparing to other encryption algorithms AES-CBC-256 provides a high level of security due to its large 256-bit key size. The key length makes it highly resistant to brute-force attacks. This structure allows for parallel processing and is suitable for various data formats. The proposed work aims to utilize the AES-CBC-256 algorithm for file encryption and decryption, providing a strong level of security with a 256-bit key. This approach adheres to industry standards and offers efficient computational performance, making it a trustworthy option for protecting confidential information.

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data Set Development for Gesture Recognition and Implementation using MATLAB and Xilinx Vivado

Data Set Development for Gesture Recognition and Implementat...

引用

Evolutionary algorithms and Soft computing Techniques (EASCT), international conference on

作者： Sowmya Madhavan Ashwini N Hegde Dept. of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bangalore India

Gesture recognition system is an algorithmic based system which can interpret different hand gestures, which are useful in human machine interface. In this paper, two datasets are used for training and testing. The first data set developed by the authors, has a total of 250 images, across 10 classes. Images of ten different hand gestures of one of the authors were captured and segregated into ten corresponding classes. For each hand gesture, 25 different images are captured in varying positions. To increase number of images in the data set, a second data set which consists of 1000 images, across 10 classes, is also considered from an open source. In the first data set, 250 images are considered for training and the resolution of each image is 432 x 578. In the second data set, 1000 images are used for training. The trained images are converted into gray scale and stored in the binary format. A gesture recognition algorithm is designed and 90 images are used for testing in both datasets. The gesture recognition algorithm is modeled in MATLAB for accuracy of 99.2% for the first data set and 98.6% for the second data set. The next stage of the project is conversion of designed MATLAB code into verilog code using HDL coder. The verilog code is functionally simulated, synthesized, and implemented in Xilinx vivado.

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Walking to Authenticate: Identifying Robust Behavioral Biometrics from Step Count data

Walking to Authenticate: Identifying Robust Behavioral Biome...

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international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC

作者： Zhen Chen Keqin Shi Weiqiang Sun Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

Step count data, readily available from smartphones and wearable devices, offers valuable insights into a user’s physical activities and lifestyle patterns, positioning it as a viable contender for integration into biometric authentication systems. In this study, we introduce Step Count Print (SCP), a novel behavioral biometric derived from coarse-grained minute-level step count data, featuring daily step count distribution to capture an individual’s unique physical activity pattern. We conduct an ablation study with data from 100 users collected over a five-year period, utilizing mainstream machine learning algorithms. Our experimental results demonstrate SCP’s non-redundancy in user authentication scenarios, achieving an impressive average accuracy of $92.3 \%$. We showcase the universality and effectiveness of SCP across various classification algorithms through user accuracy histograms. Our proposed strategy holds promise for user-friendly and reliable biometric authentication, leveraging step count data to enhance security and usability in diverse applications.

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Dibit-Based 4-Bit Parity Generator Using Reflective Semiconductor Optical Amplifier and Frequency Encoding Scheme 5th

Dibit-Based 4-Bit Parity Generator Using Reflective Semicond...

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5th international conference on Communication, Device and Networking, ICCDN 2021

作者： Bosu, Surajit Bhattacharjee, Baibaswata Department of Physics Bankura Sammilani College West Bengal Bankura India Department of Physics Ramananda College West Bengal Bishnupur Bankura India

ISBN: (纸本)9789811920035

Future generation communication and computation system are dealing with ultra-high-speed data transfer, systematic management as well as lower cost. So the researchers are motivated to implement the different logic gates, digital devices, combinational circuits, optical computers. In optical computing systems, the parity generator is familiar as an essential part. In the case of all-optical devices design, reflective semiconductor optical amplifiers (RSOAs) appear as a promising candidate. Due to the versatile gain medium of RSOA, it has various important applications in passive optical networks. In comparison with semiconductor optical amplifiers (SOAs), RSOAs exhibit better gain performance because of their double pass property. Therefore, a frequency encoded dibit-based 4-bit parity generator is designed using low-cost RSOA. It shows also better switching properties. This design operates at a high speed. This device is benefited with dibit-based logic as reduces bit error problem. MATLAB Simulink (R2018a) software is used to verify the proposed design. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical data processing

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Key Cryptographic Methods in the Cloud: A Comparative Study

Key Cryptographic Methods in the Cloud: A Comparative Study

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international conference on Intelligent Communication and Computational Techniques (ICCT)

作者： Vikas Yadav Manoj Kumar Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Hisar India

With cloud computing becoming an inseparable part of human daily life and making its way in every field of technology during the last decade with smarter human generation and adoption of new data policies, data security has become a significant pillar of trust. The standard of data security ensures the quality of privacy while availing various services over the internet. To achieve a step forward against always developing exploiters various algorithms have been developed which come under the wing of cryptography. Cryptography is the branch that ensures confidentiality, integrity, authentication, privacy, and security of the data of a consumer. This paper has dis-cussed and analyzed DES, triple DES, Blowfish, AES, IDEA, RC4, RSA, and ECC cryptography algorithms based on their ability of key size, block size, encryption time, decryption time, and total time in the Node JavaScript environment. Since, JavaScript powers 80% of the internet, this paper provides a production-level real-world analysis of various algorithms at present time.

关键词： Industries Cloud computing data privacy Ciphers Costs Elliptic curve cryptography Boosting

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