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2019 International Conference on Computing, Power and communication Technologies, GUCON 2019

作者： Alam, Maksud Mainuddin, Mainuddin Kanaujia, B.K. Beg, M.T. Kumar, Sachin Jamia Millia Islamia Department of Electronics Communication Engineering New Delhi India Jawaharlal Nehru University School of Computational and Integrative Sciences New Delhi India SRM Institute of Science and Tech. Department of Electronics Communication Engineering Chennai Tamil Nadu India

ISBN: (纸本)9789353510985

In this communication a low profile ultrawideband microstrip antenna has been proposed to cover Wi-Max (3.5 GHz/ 5.5 GHz), WLAN (5 GHz, 5.2GHz, 5.8 GHz) and C-band wireless applications suitable for a single multifunctional device. The Wide band operation have been achieved by combining resonant modes corresponding to three rectangular slots and a C-shaped horizontal strip on the ground plane. The proposed antenna is designed on 4.4 permittivity FR-4 substrate with the help of Ansoft HFSS simulator and has been experimentally verified. The Measured and simulated results are well agreed with each other. © 2019 Galgotias University.

关键词： Microstrip antennas

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Machine learning model for predicting Peste des Petits Ruminants 1

Machine learning model for predicting Peste des Petits Rumin...

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Nyambo, Devotha G. Ngulumbi, Nguse Mduma, Neema Sinde, Ramadhani Lyimo, Tumaini The Nelson Mandela African Institution of Science and Technology School of Computational and Communication Sciences and Engineering Arusha Tanzania United Republic of Kilimanjaro International Institute for Telecommunications Electronics and Computers Computer Engineering Arusha Tanzania United Republic of

ISBN: (纸本)9798350330137

Peste des petits ruminants (PPR) is a viral disease that affects small ruminants and is prevalent in many developing countries, particularly in Africa and Asia. It can spread through direct contact, air, and contaminated feed and water. PPR can result in significant economic losses and has a detrimental impact on small ruminant production and trade. Clinical signs include fever, respiratory distress, and diarrhoea, and prevention is primarily through vaccination with a live attenuated vaccine. In this study, 24 samples were selected, pre-processed and synthesized using the Conditional Tabular Generative Adversarial Networks (CTGAN) model. Feature extraction was performed, revealing difficult-breathing as the most important feature in predicting PPR in ruminants. The study used Random Forest Classifier which was fine-tuned using Bayesian Optimization to attain an accuracy of 91%. © 2023 IEEE.

关键词： Forecasting

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Implementation of a goal programming model for solid waste management: A case study of Dar es Salaam - Tanzania

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International Journal for Simulation and Multidisciplinary Design Optimization 2017年第1期8卷 1-10页

作者： Lyeme, Halidi Ally Mushi, Allen Nkansah-Gyekye, Yaw Department of Mathematical and Computer Sciences and Engineering School of Computational and Communication Science and Engineering Nelson Mandela African Institution of Science and Technology PO Box 447 Arusha Tanzania United Republic of Department of Mathematics College of Natural and Applied Science University of Dar es Salaam PO Box 35062 Tanzania United Republic of Department of Mathematics Faculty of Science Muslim University of Morogoro PO Box 1031 Morogoro Tanzania United Republic of

In this research article, the multi-objective optimization model for solid waste management problem is solved by the goal programming method. The model has three objectives: total cost minimization, minimization of final waste disposal to the landfill, and environmental impact minimization. First, the model is solved for the higher priority goal, and then its value is never allowed to deteriorate. The model is solved for the next priority goal and so on until the problem is solved. The model was tested with real data for solid waste management system from Dar es Salaam city. The results determine the best locations for recycling plants, separating plants, composting plants, incinerating plants, landfill and waste flow allocation between them. Furthermore, the solution shows a high reduction of the amount of waste to the landfill and greenhouse gas emissions by 78% and 57.5% respectively if fully implemented compared to the current system. © 2017 H.A. Lyeme et al.

关键词： Information management

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Enhancing Diabetic Retinopathy Diagnosis with Inception v4: A Deep Learning Approach 5

Enhancing Diabetic Retinopathy Diagnosis with Inception v4: ...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Athilakshmi, R. Jansi, R. Upadhyay, Roshan Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350321425

An automated system for the classification of Diabetic Retinopathy (DR) is introduced in this study. DR is a prominent cause of blindness globally, primarily impacting individuals aged 20 to 65. The proposed system can be an essential tool in the sustainable healthcare industry, providing timely and accurate diagnosis and treatment to patients with DR. Current manual screening by ophthalmologists can be time-consuming. Accurate categorization of DR is crucial to provide appropriate care and reduce the likelihood of permanent blindness. The use of big data and deep learning models in DR detection has the potential to revolutionize screening and diagnosis, leading to improved patient outcomes and reduced healthcare costs. This study compares traditional and CNN models, including Decision Trees, Random Forest, KNN, SVM, and Inception V4, to identify the best model for classifying retinal images into five stages of DR severity: No DR, Mild, Moderate, Severe, and Proliferative DR. Among the models tested, Inceptionv4 demonstrated the highest accuracy of 92.83% in classifying DR data. These findings demonstrate the potential of an automated system to improve DR diagnosis and management. © 2023 IEEE.

关键词： Computer vision

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Data Synthesis Technique for Categorical Peste des Petits Ruminants (PPR) Data Using CTGAN Model 1

Data Synthesis Technique for Categorical Peste des Petits Ru...

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

ISBN: (纸本)9798350330137

In the field of machine learning (ML), data scarcity is a serious difficulty because data gathering can be costly, time-consuming, and challenging, especially in poor nations. The complexity of using datasets to forecast livestock diseases enabling intervention and surveillance is overstated. This work provides a data synthesis method that has been applied to produce fresh data samples with accuracy from limited real-world data, in order to meet this difficulty. The ML models are trained on a large amount of data, which eliminates overfitting. We propose the synthesis of categorical data for training machine learning models for the prediction of Peste des Petits Ruminants (PPR) disease using Generative Adversarial Networks, namely the Conditional Tabular Generative Adversarial Network. The training score increased to 0.89 and the cross-validation score to 0.87 when the Random Forest algorithm was applied to the synthesized data, according to the results. The Peste des Petits Ruminants (PPR) disease can be predicted and monitored with the help of the generated dataset. With more data available, the suggested approach can be used in any area with categorical data for data-driven models and may enhance the performance of machine learning models. © 2023 IEEE.

关键词： Forecasting

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A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

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Computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

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Agent-Based Modeling and Simulation for Transmission Dynamics and Surveillance of Dengue: Conceptual and Design Model 16

Agent-Based Modeling and Simulation for Transmission Dynamic...

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16th IEEE AFRICON, AFRICON 2023

作者： Pascoe, Luba Nyambo, Devotha Godfrey Bradshaw, Karen Clemen, Thomas School of Computational and Communication Science and Engineering Nelson Mandela African Institution of Science and Technology Arusha Tanzania United Republic of Rhodes University Grahamstown South Africa Hamburg University of Applied Sciences Department of Computer Science Hamburg20099 Germany

ISBN: (纸本)9798350336214

African countries need to strengthen surveillance and control of arboviral diseases such as dengue due to increased outbreaks and spread of arboviruses. Climatic, socio-environment, and ecological variables influence the spread of dengue fever in Sub-Saharan Africa. This paper presents an Agent-Based conceptual and design model for dengue fever developed using the Multi-Agent Research and Simulation (MARS) framework. The study analyzes dengue fever's spatial distribution and identifies the causal relationship between the disease and its climatic and environmental variables. Agent-based modeling (ABM) was used to comprehend the spatial patterns of variation to determine the ecological association between the observed spatio-temporal variations in dengue fever. The domain and design model of an ABM for the surveillance of dengue fever is presented based on the Overview, Design Concepts, and Details (ODD) protocol. Model input parameters and input data for the study area are also presented. The dengue ABM can be adopted and reused for modeling other diseases and other complex problems from different domains while ensuring that their unique characteristics and appropriate modifications are considered to ensure the model's validity and relevance to the new context. © 2023 IEEE.

关键词： Autonomous agents

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Prediction of crossing nodal-lines and large intrinsic spin Hall conductivity in topological Dirac semimetal Ta3As family

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npj computational Materials 2021年第1期7卷 333-337页

作者： Wenjie Hou Jian Liu Xi Zuo Jian Xu Xueying Zhang Desheng Liu Mingwen Zhao Zhen-Gang Zhu Hong-Gang Luo Weisheng Zhao Fert Beijing Institute MIIT Key Laboratory of SpintronicsSchool of Integrated Circuit Science and EngineeringBeihang UniversityBeijingChina Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdaoChina School of Physics State Key Laboratory of Crystal MaterialsShandong UniversityJinanChina Beijing Computational Science Research Center BeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of SciencesBeijingChina School of Physical Science and Technology Lanzhou UniversityLanzhouChina

Topological insulators(TIs)are considered as ideal platforms for generating large spin Hall conductivity(SHC),however,the bulk carrier problem,which is unavoidable in TIs,hinders their practical ***,topological semimetals(TSMs)have been proposed to achieve large SHC to replace ***,the ideal TSM candidates with large SHC are still *** terms of firstprinciples calculations,we predict that Ta3As family compounds exhibit complex crossing nodal-lines(CNL)properties in absence of the spin-orbit coupling(SOC).However,they transfer to Dirac TSMs under the influence of strong SOC,and present large SHC around Fermi level in ***,the SHC value of Ta_(3)Y(Y=As,Sb,Bi)is around 1500–1700 e/h=eTeΩ·cmT^(-1),which is comparable to noble metal Pt and much larger than TIs,Weyl TSMs,and 4d/5d transition *** work not only suggests a kind of TSM family with interesting Dirac CNL around Fermi level,but also paves the way for searching large intrinsic SHC materials in complex CNL TSM systems.

关键词： topological Dirac conductivity

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On Steady Dividend Payment under Functional Mean Reversion Speed

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Journal of Mathematical Finance 2016年 6卷 368-377页

作者： Adeline Peter Mtunya Philip Ngare Yaw Nkansah-Gyekye School of Computational and Communication Science and Engineering Nelson Mandela African Institution of Science and Technology Arusha Tanzania School of Mathematics University of Nairobi Nairobi Kenya

We study how firms’ management can ensure steady dividend growth and payout to the share-holders in an emerging market. We create the dividend equalization reserve account whereby during high profit some amount of money is kept in order to top up dividends during deficiency. We use a mean reversion stochastic differential equation with a functional mean reversion speed to find the optimal dividend policy with optimal dividend equalization reserve. One of our results indicates that, it is optimal to pay high dividends when we have high mean levels. Also, we realized that a higher level of volatility which implies more dividend can be paid. And high dividend can also be paid as the interest rate rises but this is more significant when the firm makes profits above average. Lastly, we compared the buffer approach to a situation where hedging was not applied and found that the buffering approach is more suitable because it gives shareholders steady dividend payments.

关键词： Investment Dividend Policy Steady Payment Functional Mean Reversion Speed Stochastic Optimal Control

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An Effective Hand Gesture Recognition using Convolutional Neural Network with Long Short-Term Memory

An Effective Hand Gesture Recognition using Convolutional Ne...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sunil, G. Saranya, A. Duraimurugan, N. Dineshkumar, R. Ramachandra, A.C. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Department of Ece Thandalam India Engineering Nitte Meenakshi Institute of Technology Department of Electronics and Communication Bengaluru India

ISBN: (纸本)9798350313413

A Hand Gesture (HG) constitutes a mode of nonverbal communication or non-vocal interaction, where discernible bodily movements convey specific messages, either independently or in combination with spoken language. Gesture recognition techniques are employed to create systems capable of transmitting information among individuals with disabilities for operating devices. In this paper, we propose the use of Electromyography (EMG) signals for classifying hand gestures through Employing both a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in combination is being used. The main problem lies in the noise signals produced by the EMG sensor. In this work, we employ a Low Pass Filter for preprocessing the signal images, followed by feature extraction using the CNN model. To reduce the high-dimensional signal and normalize the features for the classification process, we utilize LSTM. This experiment was conducted using the Ninapro DB1 dataset. This model successfully addresses the previous issues and attains an overall accuracy of 95.56%, Precision 91.90%, Recall 89.50%, and F1 score 85.50% 91.90%, then the accurate classification compares to other existing models like Bidirectional Convolutional Gated Recurrent Unit, and Deformable convolutional network models. © 2023 IEEE.

关键词： Convolutional Neural Network Electromyography Hand Gesture Long Short-Term Memory Low Pass Filter

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