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Behavioural Cloning for Serious Games in Support of Pediatric Neurorehabilitation

Behavioural Cloning for Serious Games in Support of Pediatri...

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Mediterranean Conference on control and Automation (MED)

作者： Federico Baldisseri Edoardo Montecchiani Arturo Maiani Danilo Menegatti Alessandro Giuseppi Antonio Pietrabissa Vincenzo Fogliati Francesco Delli Priscoli Dept. of Computer Control and Management Engineering (DIAG) Sapienza University of Rome Italy 4You – Association for Social Advancement Rome Italy Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy

Behavioural Cloning is a Machine Learning method concerning how a machine attempts to autonomously mimic the actions of a human, or in general a complex controller, performing a given task. This work innovatively exploits Behavioural Cloning in support of Pediatric Neurorehabilitation. In particular, an Artificial Neural Network Classifier has been implemented to autonomously adapt the difficulty, through a set of tunable parameters, of a Serious Game that was specifically developed to stimulate some relevant cognitive capabilities of the patient. Data augmentation via Behavioural Cloning allows such autonomous difficulty adaptation system to improve its classification performances and, thus, to enforce a control logic that, in turn, improves the effectiveness of the cognitive training. The system is validated through an experimental assessment on a Serious Game that trains motor coordination: experimental results of children gameplay are analyzed and discussed.

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Exploiting Hybrid Medium Access control and Relaying Strategies to Overcome Duty-Cycle Limitations in LoRa-Based Sensor Networks

Exploiting Hybrid Medium Access Control and Relaying Strateg...

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IEEE Instrumentation and Measurement Technology Conference

作者： Tommaso Fedullo Aamir Mahmood Federico Tramarin Alberto Morato Mikael Gidlund Luigi Rovati Dept. of Management and Engineering University of Padova Dept. of Engineering “Enzo Ferrari” University of Modena and Reggio Emilia Modena Italy Dept. of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden National Research Council of Italy CNR-IEIIT Padova Italy

The industrial Internet-of-things (IIoT) paradigm is reshaping the way industrial measurement systems are designed. Industrial systems require collecting accurate and timely measurements from the field using smart sensor networks distributed in wide production areas. In this context, wireless connectivity of sensors acquires undeniable importance, and in turn, opens sig-nificant research challenges. Therefore, the research community is actively analyzing the suitability of different wireless technologies, for instance, Wi-Fi, 5G-and-beyond, and low-power wide-area networks (LPWANs), toward their possible industrial applications and optimizing them to realize high-performance and accurate smart measurement systems. In this paper, we focus on long range (LoRa)-based LPWANs (i.e., LoRaWAN), especially to overcome the duty cycle (DC) limitations of the adopted ALOHA-based medium access control (MAC) strategy in the industrial, scientific, and medical (ISM) bands. The ISM bands are subjected to an hourly constraint on the number of packet transmissions or inter-message delay, where the devices using higher spreading factors (SFs) can quickly consume the available transmission time. In this paper, we propose and assess the hybrid MAC designs in a LoRa network by combining carrier sense multiple access (CSMA) with ALOHA in two different ways i) exploiting different channel plans for the access mechanisms, ii) relay-assisted access, with devices using small SFs assisting neighboring higher-SF devices with listen-before-talk (LBT) mechanism. Our simulation results reveal that the proposed access strategies lead to a higher packet delivery rate (PDR) as well as lower mean and standard deviation of the communication delay; thus, increasing the overall measurement accuracy.

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Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing 3

Advanced Machine Learning Frameworks for Multimodal Image an...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ramesh Naidu, P. Shandilya, Saurabh Srividya, C.N. Gujar, Shantanu Sudhir Devi, Supriya Dankan Gowda, V. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560064 India Poornima College of Engineering Department of Advance Computing Rajasthan Jaipur India BGSIT Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of ECE B G Nagara571448 India Rochester Institute of Technology Golisano College of Computing and Information Sciences Department of CyberSecurity RochesterNY14623 United States Alliance University Department of Mathematics Karnataka Bangalore562106 India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354171

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework;the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework's efficiency in predicting the multimodal input and demonstrate superiority over the prior methods. © 2024 IEEE.

关键词： Deep learning

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A Study on Gynecological Cancers Using Artificial Intelligence: A Revolutionary Approach

A Study on Gynecological Cancers Using Artificial Intelligen...

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Intelligent control, Computing and Communications (IC3), International Conference on

作者： Kamal Upreti Sheela Hunekari Shitiz Upreti Ashish Kumar Rai Mohammad Shabbir Alam Mohammad Shahnawaz Nasir Dept. of Computer Science CHRIST University Delhi- NCR Campus Ghaziabad India School of Engineering and Technology Pimpri Chinchwad University Maharashtra India Department of Management Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala Haryana India Faculty of Commerce & Management Ramchandra Chandravansi University Jharkhand India Dept. of Computer Science CMJ University India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

An examination of the role of artificial intelligence (AI) in ovarian, cervical, and breast cancer early detection and management is presented in this paper. Artificial intelligence (AI) can improve diagnostic accuracy, streamline treatment protocols, and facilitate personalized medicine approaches by leveraging advancements in machine learning (ML) and deep learning (DL). Various Artificial Intelligence models have demonstrated success in improving the outcomes of cancer diagnostics, including their ability to distinguish benign from malignant tumors. Technology challenges and ethical issues related to the integration of AI into clinical practice are also discussed in the review. Specifically, we want to illustrate how artificial intelligence can lead to better prognoses and reduced mortality rates for cancer patients by enhancing early detection capabilities.

关键词： Ethics Protocols Reviews Precision medicine Malignant tumors Mortality Medical services Oncology Artificial intelligence Intelligent control

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A Digital Healthcare Service Architecture for Seniors Safety Monitoring in Metaverse

A Digital Healthcare Service Architecture for Seniors Safety...

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Metaverse Computing, Networking and Applications (MetaCom), IEEE International Conference on

作者： Qian Qu Ronghua Xu Han Sun Yu Chen Sumantra Sarkar Indrajit Ray Dept. of Electrical & Computer Engineering Binghamton University Binghamton NY USA School of Management Binghamton University Binghamton NY USA Dept. of Computer Science Colorado State University Fort Collins CO USA

We have been witnessing an unprecedented increase in the aging population in human history. It is nontrivial to ensure the health and safety of seniors living alone. The prohibitive human labor cost necessitates more sustainable, technology-oriented approaches instead of labor-intensive solutions. The raising digital healthcare services (DHS) leveraging the Internet of Medical Things (IoMT), Digital Twins (DT), and advanced fifth-generation and beyond (B5G) wireless communication technology, are widely recognized as promising solutions. By enabling a seamless interwoven of the physical world and cyberspace, Metaverse makes an ideal home for the next generation of DHS. Thanks to characteristics of decentralization, traceability, and unalterability, Blockchain is envisioned to enhance security properties in Metaverse. This paper proposes MetaSafe, a DHS architecture for seniors’ safety monitoring in Metaverse. Based on monitoring data collected by sensors, the activities and status of seniors, who are considered as the physical objects (PO), are mirrored to corresponding logical objects (LO) in a virtual community in the Metaverse, where activity recognition, potential risk prediction, and alert generation are realized. By leveraging Non-Fungible Token (NFT) technology to tokenize identities (POs and LOs) and data streams of the DHS on the blockchain, an NFT-based authentication fabric allows for verifiable ownership and traceable transferability during the data-sharing process. Specifically, an instant alerting system is introduced in this work that leverages a hybrid algorithm combining the singular spectrum analysis (SSA) approach with the long-short-term memory (LSTM) networks. Through an extensive experimental study, MetaSafe is validated as a feasible and promising approach to protect seniors living alone.

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Bayesian Belief Network Analysis for SPAD System in Railways

Bayesian Belief Network Analysis for SPAD System in Railways

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Multimedia Processing, Communication & Information Technology (MPCIT), International Conference on

作者： Manjunath Sargur Krishnamurthy Bommisetty Rahul Chandrahas Reddy Bondala M Vishnu Vardhan Reddy Magha Raghul G Ambika B J JP Morgan & Chase Co. Houston Texas USA Dept. of AI&ML BMS Institute of Technology and Management Bengaluru Dept. of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Karnataka

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

Even with a powerful network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even today. These occurrences, known as Signal Passed At Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach Bayesian Network (BN) is considered to be a better model to represent this situation when it comes to handling complexity. Bayesian Network allows for integration and includes systematic knowledge about the system, which helps to get a very flexible concise, and simple graphed representation. This study demonstrates applications of Bayesian Belief Networks (BBN) analysing and enlightening the Signal Passed at Danger (SPAD) system in railway processes. Execution of BBN offers more dynamic and flexible models compared to outmoded Fault Tree Analysis (FTA), struggling with complexity. BBN efficiently catches the inter dependencies between factors that are contributing to SPAD events, such as signal conditions, human errors, and mechanical issues. Over simulations and probability calculations using OpenMarkov software, Study revealed key intuitions, such as identifying highly risk apparatuses within SPAD system. The outcomes include enhanced risk calculation accuracy and recommendations for improving signal systems and safety protocols. These conclusions contribute to a safer and reliable railway structure, highlighting the success of BBN for complex, safety critical systems in real world applications.

关键词： Analytical models Systematics Rail transportation Software Bayes methods Safety Complexity theory Software reliability Single-photon avalanche diodes Fault trees

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Securing Sensitive Data: How Blockchain Transforms Privacy in Healthcare, Law, and Education

Securing Sensitive Data: How Blockchain Transforms Privacy i...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Ajay Sudhir Bale T S Arulananth S Saranya Tejashwini Gadag J Sathiamoorthy Sudhakiran Ponnuru Dept. of ECE New Horizon College of Engineering Bengaluru Karnataka India Department of Electronics and communication Engineering MLR Institute of Technology Hyderabad Telangana India Dept. of Mathematics BMS Institute of Technology & Management Bengaluru India School of Computer Science Engineering Reva University Bengaluru India Department of Computer science and Design RMK Engineering college Kavarapettai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

In the field of healthcare, law, and education, Blockchain technology (BT) is emerging as a transformative force. This paper helps to explore the usage of BT to enhance the privacy and security in the above said domains. In healthcare industry, blockchain offers privacy and secure management of the vital electronic health records (EHR) and patient health records (PHR), boosting the data integrity and access control. In the law sector, blockchain-enabled smart contracts are providing new possibilities for the transactions automation, transparency, and security of the transactions, thoroughly facing the challenges in terms of jurisdiction and compliance with regulations like GDPR. In the field of education, blockchain technology is improvising the record-keeping, degree verification, and collaborative learning environments. The paper helps to examine the features like the effectiveness, efficiency, and security aspects of blockchain implementations in these sectors, focusing on both the potential benefits and the challenges imposed that need to be addressed for the widespread adoption in real time.

关键词： Industries Data privacy Privacy Education Smart contracts Medical services Transforms Regulation Real-time systems Blockchains

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Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Approach with Advanced Feature engineering

Stock Price Prediction Using LSTMSeq2Seq: A Deep Learning Ap...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Kalim Amzad Chy Abdul Kadar Muhammad Masum Mohammed Shamsul Alam Md. Abul Kalam Azad Md Zia Uddin Dept. of AI Innovation Liberate Labs Texas USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Business and Technology Management Islamic University of Technology Gazipur Bangladesh Software and Service Innovation Department SINTEF Digital Oslo Norway

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This paper explores stock price prediction in the context of the Bangladesh stock market using advanced deep learning methodologies, specifically the LSTMSeq2Seq model. Traditional analytical techniques often fail to capture the complex, non-linear patterns inherent in financial data. To address this limitation, our research leverages the strengths of the LSTM-Seq2Seq architecture, which is tailored to the unique dynamics of the Bangladesh market. A robust feature engineering process is employed, utilizing 67 diverse features, including market trends, stock-specific indicators, fundamental and technical variables, and key macroeconomic factors such as gold prices and foreign exchange reserves. The dataset encompasses historical stock market data from 2012 to 2022, and extensive data preprocessing is performed to handle inconsistencies. Hyperparameter optimization is conducted using grid search to ensure model performance. The LSTMSeq2Seq model is evaluated based on key metrics such as MAE, MAPE, MSE, and the R 2 score, demonstrating superior predictive capabilities compared to traditional methods. This research not only advances the application of deep learning in financial forecasting but also provides valuable insights into stock price prediction in emerging markets, addressing ethical concerns to mitigate risks like market manipulation.

关键词： Deep learning Measurement Sentiment analysis Computational modeling Time series analysis Predictive models Macroeconomics Stock markets Forecasting Long short term memory

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Mathematical Models of Heterogeneous Machine Learning Techniques for Ransomware Protection in Cyber-Physical Systems

Mathematical Models of Heterogeneous Machine Learning Techni...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Subir Gupta Sudipta Hazra Simanta Hazra Sourav Gayen Sayanti Mukherjee Anwesa Naskar Dept. of CSE (AIML) Haldia Institute Of Technology Haldia WB India Dept. of C.S.E. Asansol Engineering College Asansol WB India Dept. of C.S.E. NSHM Knowledge Campus Durgapur WB India Dept. of Computer Application Asansol Engineering College Asansol WB India Dept. of I.T. College of Engineering and Management Kolaghat Kolaghat WB India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Ransomware attacks are a growing threat to cyber-physical systems (CPS), capable of causing financial losses, downtime, and even physical harm. Using machine learning to detect and prevent these attacks is a promising approach. A study compared four machine learning techniques—support vector machines (SVM), decision trees, random forests, and neural networks—to detect ransomware attacks on CPS. The models were trained and evaluated using a dataset of real ransomware incidents. Results showed that all four techniques effectively detected ransomware attacks. Neural networks achieved the highest F1 score, while SVMs excelled in precision, recall, and accuracy. However, the differences in performance among the methods were not significant. The models detected ransomware outbreaks within an average of 12 hours, allowing businesses to mitigate the attack's effects promptly. The study concluded that machine learning could effectively detect and prevent ransomware attacks on CPS. However, it is important to note that the study used a limited dataset, highlighting the need for further research with larger datasets to confirm these findings.

关键词： Support vector machines Neural networks Machine learning Organizations Cyber-physical systems Data models Ransomware Decision trees Protection Random forests

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COVID-19 Diagnosis Algorithm Based on Convolutional Neural Networks

COVID-19 Diagnosis Algorithm Based on Convolutional Neural N...

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2024 International Conference on Electronics and Devices, Computational Science, ICEDCS 2024

作者： Han, Xinxin Xiong, Xueguan Wang, Liuling Yang, Yuxin Lu, Dandan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Southwest University of Science and Technology School of National Defense Science Mianyang China Southwest University of Science and Technology School of Economics and Management Mianyang China Southwest University of Science and Technology School of Information Engineering Mianyang China Southwest University of Science and Technology Dept. Engineering Technology Center Mianyang China

ISBN: (纸本)9798331527624

This study investigates an automated COVID-19 diagnosis system based on convolution networks, which aims to improve the effectiveness and accuracy of diagnosis. In a systematic analysis of 1709 chest X-rays, the research team used the ResNet model of deep learning for training and evaluation. Based on a detailed analysis of the confusion matrix generated by the model, the study found that the weighted average accuracy of the model and the recall rate reached 94,04% with an F1 value of up to 0,9403. This data shows better performance of convolutional neural networks in automated diagnosis. The results of the study show that neural network technology can effectively relieve the workload of doctors and provide diagnostic support for future public health events. © 2024 IEEE.

关键词： COVID-19

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