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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Wani, Suhail Rashid Attri, Shree Harsh Setia, Sonia Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

The study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as cardiovascular disease (CVD), Chronic renal illness, and Chronic Mellitus Diabetes offer significant global health issues, using existing diagnostic procedures that are time-consuming and error-prone. The report emphasizes the rising burden of chronic illnesses, estimating that 73% of deaths will be related to these ailments by 2025. Recognizing the links between illnesses such as CKD and CVD is critical for optimal care, with a particular emphasis on the increased prevalence and health hazards associated with Chronic Diabetes Mellitus (CDM). The fundamental goal of the research is to create an effective prediction model for early illness diagnosis by employing an adaptable machine learning model that can adapt to a variety of datasets. The review of the literature looks at several machine learning algorithms that have been utilized in prior research, emphasizing their potential for automating illness identification. The methods section describes the procedures required in collecting data, preparing it, and modeling it using ensemble learning. Metrics of performance evaluation confirm the Extra tree classifier's advantage and it performs the best among all the four ensemble techniques and improves the accuracy of predicting diseases. Finally, the above investigation highlights the promise of computational learning in solving the complex issues faced by chronic illnesses, emphasizing the crucial need for accurate early disease prediction in the context of a growing global health crisis. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cat Boost Chronic Diabetes Mellitus Chronic Disease Computational learning Ensemble Model Feature Selection Heart Disease Random Forest XG Boost

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Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance 8

Goal-Reaching Policy Learning from Non-Expert Observations v...

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8th Conference on Robot Learning, CoRL 2024

作者： Huang, Renming Liu, Shaochong Pei, Yunqiang Wang, Peng Wang, Guoqing Yang, Yang Shen, Hengtao School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China Donghai Laboratory Zhejiang Zhoushan China

In this work, we address the challenging problem of long-horizon goal-reaching policy learning from non-expert, action-free observation data. Unlike fully labeled expert data, our data is more accessible and avoids the costly process of action labeling. Additionally, compared to online learning, which often involves aimless exploration, our data provides useful guidance for more efficient exploration. To achieve our goal, we propose a novel subgoal guidance learning strategy. The motivation behind this strategy is that long-horizon goals offer limited guidance for efficient exploration and accurate state transition. We develop a diffusion strategy-based high-level policy to generate reasonable subgoals as waypoints, preferring states that more easily lead to the final goal. Additionally, we learn state-goal value functions to encourage efficient subgoal reaching. These two components naturally integrate into the off-policy actor-critic framework, enabling efficient goal attainment through informative exploration. We evaluate our method on complex robotic navigation and manipulation tasks, demonstrating a significant performance advantage over existing methods. Our ablation study further shows that our method is robust to observation data with various corruptions. © 2024 Proceedings of Machine Learning Research.

关键词： Adversarial machine learning

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Image Analysis and Machine Learning in Malaria Parasite Detection: Recent Advances and Future Perspective 4th

Image Analysis and Machine Learning in Malaria Parasite Dete...

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Aggarwal, Gunjan Goyal, Mayank Kumar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819736898

Plasmodium parasites cause malaria, a deadly disease that continues to pose a significant global health burden, particularly in resource-limited regions. Detecting and classifying the parasite accurately and promptly are crucial for controlling the progression of the disease. Malaria detection and classification are most frequently performed using a single-class classification approach. Single-class classification approaches include image-based classification, feature-based classification, and machine learning-based classification. Data from multiple classes are used in multi-class classification, which is more complex than single-class classification. These methods are often more extensive than single-class techniques since they can identify and categorize several Plasmodium species and their life stages. Ensemble learning, fuzzy logic-based classification, and deep learning-based classification are some of the common multi-class classification techniques. In addition to their advantages, each approach has its own disadvantages. An in-depth review of existing single-class and multi-class approaches for malaria classification is provided in this paper to gain a better understanding of the different approaches. In the end, this study attempts to present a thorough analysis of the current approaches for classifying malaria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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Comprehensive Analysis of Cybersecurity: Examining Existing Literature and Identifying Potential Areas for Future Research 1

Comprehensive Analysis of Cybersecurity: Examining Existing ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Rani, Poonam Sehrawat, Harkesh University Institute of Engineering and Technology Maharshi Dayanand University Computer Science & Engineering Rohtak India

ISBN: (纸本)9798350356816

In recent years, many scholars and experts have discovered that mobile communication systems and techniques are susceptible to a wide range of cyberattacks. These assaults not only inflict pain and damage to private firms and also pose a threat to government institutions. The attackers employ innovative methods to undermine the security systems, utilizing sophisticated tools and strategies to bypass encryption algorithms of any magnitude. Consequently, the protection of private and sensitive data is compromised. Several innovations are now being developed to mitigate these threats. This article provides a comprehensive overview and evaluation of many advanced cyber security standards, as well as the obstacles encountered in the field of cyber security. The latest generation of cyber-attacks are thoroughly researched and documented, particularly focusing on complex key management techniques. The main objective of this article is to comprehensively analyze multiple categories of cyber security, its importance, applications, and the challenges faced in the field of cybersecurity. Cybersecurity is the practice of protecting the data and ensuring the reliability of computer assets that are either part of or associated to an enterprise's network. The objective is to defend these assets from any individuals or groups that pose a threat, at any stage of a cyber-attack. © 2024 IEEE.

关键词： Cyber attacks

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A Survey on AI based Chatbots for Emotional Health Support 15

A Survey on AI based Chatbots for Emotional Health Support

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Baby, Vadlana Pimpalshende, Anjusha Sunil, Manthena Rithin, Meesala Vamshidhar, Mothukuri Saket, Palarapu Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331300579

The revolutionary potential of emotional chatbots in improving human health is explored in this research. We research the operation of various chatbot kinds in order to reveal important qualities necessary for the development of highly efficient systems. Our examination, which ranges from rule-based to machine learning-based models, provides light on the intricacies of each kind, leading to a better understanding of their mechanics. We highlight critical factors for creating emotionally intelligent chatbots, emphasizing the necessity of adaptive learning, natural language processing, and context awareness. This investigation serves as a starting point for developers, providing insights into the inner workings of various chatbot designs and paving the way for the creation of smart, user-centric, and efficient chatbots poised to revolutionize healthcare support. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Improving Pneumonia Diagnosis via Deep Learning: A Comprehensive Approach Incorporating CNN Classification 5th

Improving Pneumonia Diagnosis via Deep Learning: A Comprehen...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Mallidi, S. Kumar Reddy Vanapalli, RamaDevi Jella, D. S. Priyanka Nimmakayala, Tejaswi Narni, Devi Attili, Chandini Computer Science and Engineering Sri Vasavi Engineering College Andhra Pradesh Tadepalligudem534101 India Computer Science and Technology Sri Vasavi Engineering College Andhra Pradesh Tadepalligudem534101 India

ISBN: (纸本)9783031770777

Amidst the backdrop of the COVID-19 pandemic and the pressing global health challenge of lung disease, this study presents a pioneering approach to bolster diagnostic evaluations. Focused on harnessing the power of deep learning techniques, our research endeavors to navigate the intricacies of lung disease diagnosis. A novel hybrid CNN model is introduced, tailored to proficiently identify and scrutinize lung abnormalities, with specific attention to the unique complexities posed by COVID-19. Through comparative analyses against standalone CNN models like VGG16, ResNet50, VGG19and InceptionV3, we showcase the superiority of our hybrid model. Remarkably, our proposed model attains heightened efficiency while employing fewer parameters and effectively addressing overfitting concerns commonly encountered in deep learning methodologies. This study represents a significant stride forward in the realm of lung disease diagnostics, furnishing a more robust and precise approach for comprehensive assessment, particularly within the COVID-19 landscape. This advancement contributes substantially to the evolution of sophisticated diagnostic tools, ultimately enhancing patient care and outcomes in the global battle against lung disease. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Lung cancer

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Existing Approaches & A Proposal to Content Analysis of Pilots' Speech Activities

Existing Approaches & A Proposal to Content Analysis of Pilo...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Bogdanova, Diana Giniiatullin, Azamat Batth, Jaspreet Singh Ufa University of Science and Technology Institute of Computer Science Mathematics and Robotics Ufa Russia Chandigarh University Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350395044

Content analysis of pilot speech is a key tool for improving flight safety as it allows to identify potential problems in communication between pilots, dispatchers and crew. The development of efficient algorithms for analyzing pilot speech contributes to improving communication skills, preventing incidents and improving overall airline safety. The research focuses on the possibility of applying machine learning techniques and deep neural networks to analyze speech data, which opens new perspectives for information processing and interpretation. The results of the study emphasize the interdisciplinary nature of the topic and suggest new methodological approaches, including the use of fuzzy set theory and fuzzy logic systems to handle uncertainty in speech data, which in turn helps to improve communication performance in complex and stressful flight conditions. The finalization of the work includes the evaluation of the developed methods and approaches in terms of their applicability and impact on improving flight safety, incorporating model training results and addressing testing errors. © 2024 IEEE.

关键词： Deep neural networks

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An Extrinsic LiDAR-GNSS/INS Registration Approach for Autonomous Driving 11

An Extrinsic LiDAR-GNSS/INS Registration Approach for Autono...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Yadav, Sujata Abidi, Ali Imam Chhabra, Megha School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350350357

Ensuring precise registration of sensors stands as a pivotal element in integrating LiDAR and inertial data for autonomous driving. This study introduces an innovative three-phase method for extrinsic registration among GNSS/INS and LiDAR systems in the scenarios of autonomous vehicles. The initial phase rapidly refines extrinsic parameters among sensors by utilizing point cloud surface features, swiftly narrowing down the initial large error to a significantly smaller range. Subsequently, the second phase further refines these parameters by considering LiDAR-mapping space occupancy while eliminating any distortion caused by motion. The concluding phase rectifies z-axis errors attributed to the vehicle's plane motion, resulting in the attainment of precise extrinsic parameters. Notably, our method leverages planar environmental features, facilitating swift registration procedures. Evaluation on real-world datasets substantiates the reliability and accuracy of this approach. © 2024 IEEE.

关键词： Optical radar

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Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

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16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

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REDUCTION OF CO2 EMISSION AS A RESULT OF THE USE OF 48-VOLT ELECTRICAL INSTALLATIONS IN PASSENGER CARS

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Archives of Transport 2023年第2期66卷 7-20页

作者： Kozyra, Jacek Lukasik, Zbigniew Kuśmińska-Fijalkowska, Aldona Janota, Aleš Faculty of Transport Electrical Engineering and Computer Science University of Technology and Humanities Radom Poland Faculty of Electrical Engineering and Information Technology University of Žilina Žilina Slovakia

The introduction of a new standard of 48-volt electrical systems in cars comes at an additional cost to the vehicle. Acceptance of these costs is justified because it becomes a way to achieve lower CO2 emissions and lower fuel consumption. An important factor in favor of adopting 48-volt systems is the reduction in CO2 due to the use of a highly efficient 48-volt motor-generator unit coupled to a DC/DC converter. A methodology for testing new solutions to quantify CO2 savings and reductions therefore becomes crucial. This methodology must be capable of demonstrating the CO2 benefits primarily of the innovative technology proven in real-world driving conditions and with a large amount of realistic statistical data. The introduction of new eco-innovations must take into account the linkage and impact on other environmentally oriented ecoinnovative solutions. When implementing new technical solutions, a necessary aspect is the interaction with other innovations installed in vehicles with new electrical installation standards. Therefore, for the expected synergy of solutions to occur, two or more innovative technologies must be installed. Then the combined savings from one of them will affect the performance of the other technologies, and vice versa. The new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter fits very well in creating interactions with other implemented solutions aimed at reducing CO2 emissions. The article discusses the problems of the introduced new technology of a high-efficiency 48-volt motor-generator unit cooperating with a 48V/12V DC/DC converter. The publication analyzes the impact of increasing the voltage rating of current passenger car installations to 48V. Based on the methodology for determining the reduction of CO2 emissions of a vehicle with a 48V/12V DC/DC voltage converter installed, the mass of fuel per unit of engine operation time was determined. The amount of fuel saved was determ

关键词： Carbon dioxide

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