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A Hybrid Approach for Soil Nutrient Estimation using Multispectral Data in Belgium and Luxembourg 1

A Hybrid Approach for Soil Nutrient Estimation using Multisp...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Jain, Shagun Sethia, Divyashikha Tiwari, K.C. Delhi Technological University Department of Software Engineering New Delhi India Delhi Technological University Department of Civil Engineering New Delhi India

ISBN: (纸本)9798350342796

Planning for agricultural production and sustainable land management depend on accurately measuring soil nutrient levels. Leveraging machine learning to estimate soil nutrients simplifies farming and land management decision-making. This paper presents a novel hybrid model to forecast soil nutrient concentrations in the Belgium and Luxembourg region by combining Partial least squares regression (PLSR) with Gaussian process regression (GPR). An integrated approach is provided for predicting various soil nutrients, utilizing the Lucas topsoil dataset and multispectral Landsat 8 satellite images, extending the model's applicability to numerous agricultural and environmental applications. The hybrid model has undergone evaluation, producing RMSE and RPD values within acceptable ranges for various soil parameters, such as Soil Organic Carbon (SOC), Electrical Conductivity (EC), Nitrogen (N), Potassium (K), Phosphorus (P), pH in Water (H2O), pH in Calcium Chloride (CaCl2), and pH in Calcium Carbonate (CaCO3). © 2023 IEEE.

关键词： Artificial Intelligence Lucas top soil dataset machine learning Multispectral Soil nutrients

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Scaling a machine learning Approach to many kinds of Malicious Behavior for Cybersecurity 115

Scaling a Machine Learning Approach to many kinds of Malicio...

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115th AEIT international Annual conference, AEIT 2023

作者： Casey, William Melaragno, Anthony United States Naval Academy Cyber Science Department AnnapolisMD United States

ISBN: (纸本)9788887237603

Monitoring processes and system resources to detect malware or malicious behavior at runtime is an enduring problem. Static signature detection offers little utility as malware easily bypasses signature detection with code polymorphism or obfuscation techniques. software verification offers strong assurances for behavior but has limited scope and scale. Still, systems continue to routinely execute un-certified software components, suggesting that the benefits to do so outweigh the additional costs for all but limited niche applications requiring extra security. Therefore run-time monitoring remains the defacto and universally applicable modality for malcode detection. run-time monitors, enabled by machine learning (ML) models, can detect subtle behavior changes even with single shot training, and represent the current state of the art;however little has been done to specify the requirements and unique challenges of designing monitor software so it can be used to train and robustly detect malware in systems at run-time. The continuation of threats posed by Ransomware warrants prompt detection and mitigation of malicious processes at run-time. The re-design of system monitors to enable the current state of the art is overdue. Here we address the unique challenges of monitoring systems while training and deploying behavior-based ML models. We specify the software engineering requirements of each task individually and the trade offs for system integration. Our contributions include specifying the desired properties for monitor software and showing how they correspond to mathematical properties that ease ML behavior model development and enhance ML behavior model deployment. © 2023 AEIT.

关键词： Malware

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Mining Reddit Data to Elicit Students' Requirements During COVID-19 Pandemic 31

Mining Reddit Data to Elicit Students' Requirements During C...

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31st IEEE international Requirements engineering conference Workshops, REW 2023

作者： Rahman, Shadikur Ahmed, Faiz Nayebi, Maleknaz York University Exines Lab Canada

ISBN: (纸本)9798350326918

Data-driven requirements engineering leverages the abundance of openly accessible and crowdsourced information on the web. By incorporating user feedback provided about a software product, such as reviews in mobile app stores, these approaches facilitate the identification of issues, bug fixes, and implementation of change requests. However, relying solely on user feedback about a software product limits the possibility of eliciting all requirements, as users may not always have a clear understanding of their exact needs from the software, despite their wealth of experience with the problem, event, or challenges they encounter and use the software to assist them. In this study, we propose a shift in requirements elicitation, focusing on gathering feedback related to the problem itself rather than relying solely on feedback about the software product. We conducted a case study on student requirements during the COVID-19 pandemic in a higher education institution. We gathered their communications from Reddit during the pandemic and employed multiple machine-learning and natural language processing techniques to identify requirement sentences. We achieved the F-score of 0.79 using Naive Bayes with TF-IDF when benchmarking multiple techniques. The results lead us to believe that mining requirements from communication about a problem are feasible. While we present the preliminary results, we envision a future where these requirements complement conventionally elicited requirements and help to close the requirements gap. © 2023 IEEE.

关键词： Requirements engineering

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A machine learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Prediction of Covid-19 using machine learning and Deep learning approaches: A data driven study 1

Prediction of Covid-19 using Machine Learning and Deep Learn...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Akhter, Thanzina Barua, Labannya Sultana, Mahmuda Masum, Abdul Kadar Muhammad International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350315769

COVID-19 is generated by the SARS-CoV2 virus, which has caused an outbreak around the world. Since 2020, it has been labeled a pandemic. Due to the daily growth in the number of cases, this takes time for demonstrating the laboratory data, and as a result, treatment and resource limits have emerged. Because of such limitations the necessity for making decisions using clinical data with prognostic algorithms has emerged. Forecasting algorithms could positively relieve the pressure on healthcare systems by correctly identifying the disease. We employ the following machine learning models in this research: RF, SVM, hybrid RF-SVM and deep learning: ANN, CNN, LSTM, CNN-LSTM models and Laboratory data is being used to develop clinical forecasting models that will help medical professionals to figure out the patients that are most likely to develop COVID-19. Our models' prediction ability is measured using recall, F1-score, precision, accuracy and AUC scores. The models were verified using train test method and cross validation strategy. The assessment shows that most of our prediction models can reliably identify patients with COVID-19 disease, amidst the hybrid CNN-LSTM model outperforming the others with an accuracy of 89.5 percent,0.93 F1-score, 0.91 in precision,0.97 in recall, and AUC of 0.70, whereas among the machine learning models the hybrid ensemble outperforms other ML models with an accuracy of 92%. Predictive models obtained from laboratory findings have been found to be successful in predicting COVID-19 infection, which can help medical experts administer resources more effectively. © 2023 IEEE.

关键词： COVID-19

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Multimodal Speech Emotion Recognition Using Modality-Specific Self-Supervised Frameworks

Multimodal Speech Emotion Recognition Using Modality-Specifi...

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2023 IEEE international conference on Systems, Man, and Cybernetics, SMC 2023

作者： Patamia, Rutherford Agbeshi Santos, Paulo E. Acheampong, Kingsley Nketia Ekong, Favour Sarpong, Kwabena Kun, She School of Information and Software Engineering University of Electronic Science and Technology of China SC Chengdu610054 China Flinders University Centre for Defence Engineering Research and Training College of Science Engineering TonsleySA5042 Australia

ISBN: (纸本)9798350337020

Emotion recognition is a topic of significant interest in assistive robotics due to the need to equip robots with the ability to comprehend human behavior, facilitating their effective interaction in our society. Consequently, efficient and dependable emotion recognition systems supporting optimal human-machine communication are required. Multi-modality (including speech, audio, text, images, and videos) is typically exploited in emotion recognition tasks. Much relevant research is based on merging multiple data modalities and training deep learning models utilizing low-level data representations. However, most existing emotion databases are not large (or complex) enough to allow machine learning approaches to learn detailed representations. This paper explores modality-specific pre-trained transformer frameworks for self-supervised learning of speech and text representations for data-efficient emotion recognition while achieving state-of-the-art performance in recognizing emotions. This model applies feature-level fusion using nonverbal cue data points from motion capture to provide multimodal speech emotion recognition. The model was trained using the publicly available IEMOCAP dataset, achieving an overall accuracy of 77.58% for four emotions, outperforming state-of-the-art approaches © 2023 IEEE.

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Epileptic Seizure Detection based on Hyperparameter Optimization using EEG Data 2

Epileptic Seizure Detection based on Hyperparameter Optimiza...

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2nd international conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Poorani, S. Kalaiselvi, S. Aarthi, N. Agalya, S. Malathy, N.R. Abitha, M. Computer Technology-UG Kongu Engineering College Erode India

ISBN: (纸本)9781665491990

Epilepsy is a common nerve disorder resulting in seizures. Seizure is best defined as an abnormal electrical activity that can affect brain's function. EEG Signals are essential for epileptic seizure detection since they show the rise in electrical activity in the brain. machine learning allows software applications to predict outcomes precisely without being explicitly programmed. Artificial Neural Network (ANN) is the popular classifier in machine learning (ML) that categorizes EEG data and detects seizures efficiently without compromising performance. ANN architecture includes several parameters like neurons, activation function, optimizers and so on. For accurate prediction, not all the datasets need the same set of these parameters. So tuning these parameters according to the dataset is important. The proposed work focus on three different methods (Bayes Optimization, Gird Search and Random Search) to search the best values of hyperparameters such as activation function, batch size, epochs, learning rate, number of neurons, and optimizer that support the model to achieve best performance on a given dataset. © 2023 IEEE.

关键词： Electroencephalography

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6th international conference on Innovative Computing and Communication, ICICC 2023

6th International Conference on Innovative Computing and Com...

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6th international conference on Innovative Computing and Communication, ICICC 2023

ISBN: (纸本)9789819940707

The proceedings contain 128 papers. The special focus in this conference is on Innovative Computing and Communication. The topics include: Wearable Sensor Based Cloud Data Analytics Using Federated learning Integrated with Classification by Deep learning Technique;comparison of Decision Tree and Random Forest for Default Risk Prediction;trusted Cloud Service Framework for Cloud Computing Security;HITR-ECG: Human Identification and Classification Simulation System Using Multichannel ECG Signals: Biometric Systems Era;brain Disorder Classification Using Deep Belief Networks;Diabetic Retinopathy Detection Using Deep CNN Architecture and Medical Prescription;an Innovative software engineering Approach to machine learning for Increasing the Effectiveness of Health Systems;region of Interest and Feature-based Analysis to Detect Breast Cancer from a Mammogram Image;vehicular Ad Hoc Network: A Review;Securing the Networks Against DDoS Attacks Using Blockchain Technology;the Proposed Deep learning Combined with Knowledge Graphs for Fake News Detections on Social Networks;algorithm-Driven Predictive Analysis of Blue-Chip Stocks in the Murky Indian Environment;survey on Crime Analysis, Forecasting, and Prediction Using machine learning;soft Computing and Data Mining Techniques for Dengue Detection: A Review;Extended HD-MAABE Scheme Supporting Delegation and Revocation for Cloud-based Organizational Data Sharing;detection of Open Metal Sites in Metal–Organic Frameworks Using machine learning;PCA-Based Feature Selection and Hybrid Classification Model for Speech Emotion Recognition;ANFIS and Kernel Extreme learning machine to the Assessment and Identification of Seismic b-value as Precursor;payroll Management Using Blockchain;centric Management of Resources in Modern Distributed Computing;enhancing Security of IoT Data Transmission in Social Network Applications.

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HealthEdge: A machine learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System 14

HealthEdge: A Machine Learning-Based Smart Healthcare Framew...

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023 Elsevier B.V.. All rights reserved.

关键词： Edge computing

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LightGBM-based line loss prediction model for distribution networks 6

LightGBM-based line loss prediction model for distribution n...

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6th international conference on Advanced Electronic Materials, Computers, and software engineering, AEMCSE 2023

作者： Wu, Xiaogang Li, Zuxin Zhou, Xiaoqing Ji, Qingfeng Mao, Shuangshuang Ju, Xiaoming Lishui Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd Lishui China Zhenghao Group Technology Network Branch Lishui China School of Software Engineering East China Normal University Shanghai China Zhejiang Jierui Electric Power Technology Co. LTD Ningbo China

ISBN: (纸本)9781510668249

Accurate calculation of line loss in distribution networks can better guide the power system on how to optimize the power grid and how to carry out technical loss reduction work. In response to the problems of many electrical parameters, complicated steps and low accuracy of results required for the calculation of theoretical line loss values in traditional distribution networks, this paper proposes a distribution network line loss prediction method based on the light gradient boosting machine (LightGBM) model algorithm. The method uses machine learning models to model key electrical parameters and line loss values to automatically calculate the grid line loss. To address the difficulty of tuning the LightGBM model, we use a Bayesian optimization algorithm to adjust the model parameters. In this paper, grid data from the Kaggle data platform is used for analysis and validation, and the experimental results show that the model proposed in this paper has higher prediction accuracy than the traditional BP model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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