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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Thobeka Langa Hein S. Venter Dep. of Computer Science University of Pretoria Pretoria South Africa

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Machine Learning (ML) has emerged as a constantly evolving and powerful technology with the potential to transform the field of information security. Its ability to handle enormous quantities of data at high speeds, detect abnormalities, and automate efficient responses to security risks makes ML very promising in this domain. However, its application in the field of information security has not been extensively investigated, and a comprehensive understanding of existing research gaps in ML-based information security is still lacking. To accomplish this objective, the authors use previous publications to conduct an analysis of the machine learning techniques that have been adopted or explored within the field of information security. The outcome is then used to produce a state-of-the-art paper, identifying areas where more research is required.

关键词： Deep learning Training Computational modeling Supervised learning Digital forensics information security Intrusion detection Reinforcement learning Transforms Labeling

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A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka 9

A Smart Aquaponic System for Enhancing The Revenue of Farmer...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Ekanayake, Dhanushka De Alwis, Pubudu Harshana, Pasindu Munasinghe, Dilusha Jayakody, Anuradha Gamage, Ms. Narmada Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Sri Lanka's agricultural sector confronts serious challenges from fertilizer shortages and agriculture-related chemical scarcity. Innovations comparable to aquaponic systems may be offered to Sri Lankan farmers to overcome these difficulties using IoT and ML technology. This research scope is to implement a smart and secure aquaponic environment monitoring system to forecast plant and fish growth factors, provide Sri Lankan farmers with insights into the environment's behaviors, and take measures according to the predictions utilizing control mechanisms. In this research, more exact predictions have been generated by the Random Forest algorithm model rather than the LSTM model, and most of the investigated parameters given good accuracy according to the absolute mean error (Media TDS-1.95, Media pH-0.06, Media Temperature-0.49, Env. Temperature- 0.94, Env. Humidity-2.70) except the environment light intensity (64.11). The ML solution studied in this research paper would increase the quality of traditional agriculture in Sri Lanka for greater productivity and economic benefit. © 2022 IEEE.

关键词： Internet of things

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbor...

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IEEE International Workshop on information Forensics and Security (WIFS)

作者： Matteo Varotto Florian Heinrichs Timo Schürg Stefano Tomasin Stefan Valentin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Sciences Germany

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning (ML). We propose to detect jamming at the physical layer with an ML model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.

关键词： Support vector machines Accuracy 5G mobile communication Computational modeling Wireless networks Nearest neighbor methods Convolutional neural networks Jamming Principal component analysis Narrowband

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Topics Analysis and Trends on Blockchain Applications in the Energy Sector

Topics Analysis and Trends on Blockchain Applications in the...

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Software Analysis, Evolution and Reengineering - Companion (SANER-C), IEEE International Conference on

作者： Matteo Vaccargiu Giacomo Ibba Andrea Pinna Dep. of Mathematics and Computer Science University of Cagliari Cagliari Italy

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

Blockchain technology is increasingly finding its way into application areas as it offers major improvements in efficiency, security, and transparency in a wide range of activities. Among others, blockchain has enormous potential for influence in the energy sector, particularly in the field of renewable energy. This paper analyzes the most interesting applications of this technology in the energy sector from the topics most discussed on technical forums. Specifically, this study seeks to identify and examine the most discussed topics through a topic analysis on a dataset of articles extracted from CoinDesk. It is proposed, from these texts, to identify important topics related to energy markets, energy communities, energy traceability, and energy certification. The results were obtained using a Bidirectional Encoder Representations from Transformers (BERT) model for deep topic analysis. BERT provides detailed insight into blockchain technology and renewable energy discussions using natural language processing to extract latent topics and trends. This research attempts to contribute to the existing knowledge set by offering a systematic analysis of the most important topics in the application of blockchain to the energy sector. The results of this study can help technologists identify the interests of the community and foster progress in integrating blockchain into the renewable energy paradigm.

关键词： Renewable energy sources Analytical models Systematics Bidirectional control Market research Transformers Encoding

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A FAST ALGORITHM FOR CONSISTENCY CHECKING PARTIALLY ORDERED TIME

arXiv

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arXiv 2023年

作者： Eriksson, Leif Lagerkvist, Victor Dep. Computer and Information Science Linköpings Universitet Sweden

Partially ordered models of time occur naturally in applications where agents or processes cannot perfectly communicate with each other, and can be traced back to the seminal work of Lamport. In this paper we consider the problem of deciding if a (likely incomplete) description of a system of events is consistent, the network consistency problem for the point algebra of partially ordered time (POT). While the classical complexity of this problem has been fully settled, comparably little is known of the fine-grained complexity of POT except that it can be solved in O∗((0.368n)n) time by enumerating ordered partitions. We construct a much faster algorithm with a run-time bounded by O∗((0.26n)n). This is achieved by a sophisticated enumeration of structures similar to total orders, which are then greedily expanded towards a solution. While similar ideas have been explored earlier for related problems it turns out that the analysis for POT is non-trivial and requires significant new ideas. © 2023, CC BY.

关键词： Complex networks

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Improved Algorithms for Allen's Interval Algebra by Dynamic Programming with Sublinear Partitioning

arXiv

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arXiv 2023年

作者： Eriksson, Leif Lagerkvist, Victor Dep. Computer and Information Science Linköpings Universitet Sweden

Allen’s interval algebra is one of the most well-known calculi in qualitative temporal reasoning with numerous applications in artificial intelligence. Recently, there has been a surge of improvements in the fine-grained complexity of NP-hard reasoning tasks, improving the running time from the naive 2O(n2) to O*((1.0615n)n), with even faster algorithms for unit intervals a bounded number of overlapping intervals (the O*(·) notation suppresses polynomial factors). Despite these improvements the best known lower bound is still only 2o(n) (under the exponential-time hypothesis) and major improvements in either direction seemingly require fundamental advances in computational complexity. In this paper we propose a novel framework for solving NP-hard qualitative reasoning problems which we refer to as dynamic programming with sublinear partitioning. Using this technique we obtain a major improvement of O*((logcnn)n) for Allen’s interval algebra. To demonstrate that the technique is applicable to more domains we apply it to a problem in qualitative spatial reasoning, the cardinal direction point algebra, and solve it in O*((logcnn)2n/3) time. Hence, not only do we significantly advance the state-of-the-art for NP-hard qualitative reasoning problems, but obtain a novel algorithmic technique that is likely applicable to many problems where 2O(n) time algorithms are unlikely. © 2023, CC BY.

关键词： Dynamic programming

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Real-time System for Translating American Sign Language to Text Using Robust Techniques

Real-time System for Translating American Sign Language to T...

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2023 International Conference on Recent Advances in science and Engineering technology, ICRASET 2023

作者： Dabwan, Basel A. Jadhav, Mukti E. Abosaq, Hamad A. Olayah, Fekry A. Al Yami, Mohammed Abdelrahman, Yahya A. AlBaydha University Department of Information System AlBaydha Yemen Shri Shivaji Science & Art College Department of Computer Science India Najran University Computer Science Dep. Saudi Arabia Najran University Kingdom of Saudi Arabia Department of Information System Saudi Arabia Najran University Faculty of Computer Science and Information Systems Department of Information Systems Saudi Arabia Najran University College of Computer Science and Information Systems Department of Information System Saudi Arabia

ISBN: (纸本)9798350306927

Sign language serves as a vital medium of communication for the Deaf and dumb people. We present a real-time system to translate ASL into text, addressing the communication gap between deaf and dumb people and normal people. Our system leverages computer vision and machine learning techniques to accurately recognize ASL gestures. In this study, we introduce a convolutional neural network (CNN) and (LSTM) approaches designed to identify hand gestures associated with human actions as captured through images from cameras. By utilizing the hand's spatial positioning and orientation, we construct the training and testing datasets for the CNN-LSTM model, we were used 4680 skeleton images of the ASL alphabet, from A to Z as a dataset. The hand image undergoes an initial filtering process followed by classification, where a classifier determines the classification of the hand gesture. Subsequently, the refined images are harnessed to train the CNN-LSTM model. Our real-time system provides accurate and reliable results in real-time 99.9%, in addition to generating new datasets that are processed and can be trained on any model. © 2023 IEEE.

关键词： Real time systems

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Precision Diagnosis in Pandemics: Machine Learning and Cough Sound Analysis for COVID-19

Precision Diagnosis in Pandemics: Machine Learning and Cough...

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International Conference on information and Communication Technologies for Disaster Management (ICT-DM)

作者： Iyad Chehili Skander Hamdi Dep. of Artificial Intelligence and Data Science University of Science and Technology Houari Boumediene Algiers Algeria Dep. of Computer Science University of Ferhat Abbes Setif I Setif Algeria

ISBN: (数字)9798350367928

ISBN: (纸本)9798350367935

The unprecedented global crisis caused by the COVID-19 pandemic has significantly impacted public health, economies, and daily routines. A critical challenge in managing the pandemic is promptly and precisely identifying COVID-19 cases. As a result, there is an urgent need for alternative, non-invasive, and swift diagnostic techniques to identify infected individuals and mitigate the spread of the virus. In this study, we introduce a machine-learning method for early COVID-19 diagnosis by analyzing cough sounds. The study utilizes Mel-frequency cepstral coefficients, extracted from cough audio. Extensive experiments using the COUGHVID cough dataset demonstrate the feasibility and effectiveness of the approach which uses the Extra-Tree classifier. The classification model outperforms other models with a testing accuracy of 90.05%, sensitivity of 86.01%, and an area under the curve of 90.08%, outperforming other similar works, in terms of performance and computation.

关键词： COVID-19 Training Accuracy Sensitivity Pandemics computer viruses Computational modeling Random forests Standards Testing

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Using the web as corpus for un-supervised learning in question answering 16

Using the web as corpus for un-supervised learning in questi...

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16th Conference on Computational Linguistics and Speech Processing, ROCLING 2004

作者： Wang, Yi-Chia Wu, Jian-Cheng Liang, Tyne Chang, Jason S. Dep. of Computer and Information Science National Chiao Tung University Taiwan Dep. of Computer Science National Tsing Hua University Taiwan

In this paper we propose a method for unsupervised learning of relation between terms in questions and answer passages by using the Web as corpus. The method involves automatic acquisition of relevant answer passages from the Web for a set of questions and answers, as well as alignment of wh -phrases and keywords in questions with phrases in the answer passages. At run time, wh-phrases and keywords are transformed to a sequence of expanded query terms in order to bias the underlying search engine to give higher rank to relevant passages. Evaluation on a set of questions shows that our prototype improves the performance of a question answering system by increasing the precision rate of top ranking passages returned by the search engine. © Proc. of the 16th Conference on Computational Linguistics and Speech Processing, ROCLING 2004. All rights reserved.

关键词： Search engines

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Predicting Patient Sexual Function after Prostate Surgery Using Machine Learning 28

Predicting Patient Sexual Function after Prostate Surgery Us...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Rotbei, Sayna Napolitano, Luigi Zinno, Stefania Verze, Paolo Botta, Alessio University of Naples Federico II Naples Dep. Electrical Eng. and Information Technology Italy Reprod. Science and Odontostomatology University of Naples Federico II Naples Dep. Neurosciences Italy University of Naples Federico II Naples Dep. Social Sciences Italy Surgery and Dentistry University of Salerno Dept. Medicine Salerno Italy

ISBN: (纸本)9798350300482

A major health concern for men is prostate cancer. An accurate prediction of patients' conditions after surgery is essential for understanding their quality of life. For improving patient care, medical and healthcare professionals use machine learning for a variety of purposes. By using supervised machine learning algorithms, we aim to identify the most reliable predictors of patient sexual function one year after surgery. We used the EPIC-26 (Expanded Prostate Index Composite-26) questionnaire to assess the patient's quality of life and sexual function. An approximate 500 patient sample was used in our case study to test the effectiveness of this methodology. Based on demographic and clinical data collected prior to surgery, our model predicts patient self-Assessment of sexual function with high accuracy one year after surgery. In order to improve and enhance the quality of the patient experience, the methodology presented can support clinical decisions. © 2023 IEEE.

关键词： Urology

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