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Space Robotics-The Influence of Advanced Technology

Space Robotics-The Influence of Advanced Technology

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Intelligent Systems and Embedded Design (ISED), International Conference on

作者： Deepa Kumari Akshara Chauhan Ishika Chauhan Akansh Srivastava Nishant Anupam Dept.of Electrical Engineering Chandigarh University Mohali India Dept.of Computer Science Engineering Chandigarh University Mohali India

ISBN: (数字)9798331522612

ISBN: (纸本)9798331522629

Space robotics is a rapidly evolving field with profound implications for space exploration and technological advancement. This paper provides a comprehensive overview of the history, challenges, current scenario, and future opportunities in space robotics. Beginning with a historical retrospective, the paper highlights key milestones in space robotics, from the pioneering launch of Sputnik to the recent successes of Mars rovers and beyond. Challenges in both land-based and space environments are discussed, including environmental conditions, obstacle avoidance, energy efficiency, communication lag, and autonomy Additionally, advancements in humanoid robots and the growing space robotics market are highlighted. Looking to the future, the paper explores various opportunities for space robotics, including planetary exploration, asteroid mining, satellite lite servicing, space debris cleanup, infrastructure construction, deep space exploration, space tourism support, and interplanetary communication. Specific future missions, such as Mars Sample Return mission of NASA and ESA's ExoMars Rover, are discussed in detail, illustrating the breadth and depth of possibilities in space robotics. [1] Overall, this paper underscores the transformative potential of space robotics in expanding human presence beyond Earth and unlocking the mysteries of the cosmos.

关键词： Space vehicles Location awareness Mars Satellites Space missions Space debris NASA Humanoid robots History Intelligent systems

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A Collaborative Health Care System: A Pilot based on Blockchain 3

A Collaborative Health Care System: A Pilot based on Blockch...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Divya, P Bindu Jadon, Shruti People's Education Society University Dept. of Computer Science and Engineering Bengaluru India People's Education Society Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350354348

Blockchain-driven healthcare system that offers personalized, data-driven treatment options hopes to transform the medical industry. This system aims to increase diagnostic accuracy, treatment efficacy, and data security. It strongly emphasizes the value of interdisciplinary collaboration among medical professionals, enabling various professions to work together to provide comprehensive care for complex medical conditions. It also addresses the ethical and legal concerns that result from cross-specialty cooperation and the exchange of patient data, including as patient privacy and data security. © 2024 IEEE.

关键词： Differential privacy

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Enhanced Classification of Alzheimer's Disease Using Deep Learning and Machine Learning Models with MRI Scans

Enhanced Classification of Alzheimer's Disease Using Deep Le...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Salahudin, Amna Qadeer, Eeman Ahmed, Ayesha Fahad, Labiba Gillani Dept. of Software Engineering FAST National University of Computer and Emerging Sciences Islamabad Pakistan Dept. of Computer Science FAST National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9798331510503

Alzheimer's disease (AD) is a slowly progressive neurodegenerative disease, which necessitates early and accurate diagnosis for effective management. This research presents a comprehensive approach to the classification of Alzheimer's disease using Magnetic Resonance Imaging (MRI) scans, focusing on four distinct classes: nondemented, mild, very mild, and moderate demented. Our main goals are to combine diverse datasets to improve the robustness and adaptability of the model and to fully investigate Alzheimer's disease in all four stages. To achieve these goals, we employed a combination of machine learning and deep learning models. The models utilized include Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM), Gabor filters with SVM, Gabor-Gray Level Co-occurrence Matrix (GLCM) with SVM, ResNet50, AlexNet, and InceptionV3. Among these, the InceptionV3 model achieved the highest accuracy of 96.20% using 5-fold cross-validation followed the Resnet50 model achieving accuracy of 95.43%. Our approach addresses key challenges such as class imbalance, high intra-class variation, inter-class similarities, and dataset variability, demonstrating significant potential in improving Alzheimer's disease classification and detection. © 2024 IEEE.

关键词： Gabor filters

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Optimized Crop Prediction and Monitoring Using Ensemble Machine Learning Algorithms 2

Optimized Crop Prediction and Monitoring Using Ensemble Mach...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, H.S. Usha, S.M. Sheela, S.N. Anu, H. Sharmila, N. Mallikarjunaswamy, S. Basavaraju, N.M. Ravi, P. Pavithra, G.S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Dept of Computer Science and Engineering Mysuru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

This paper addresses the urgent need for enhanced agricultural practices by pinpointing the significant limitations of existing crop prediction and monitoring systems. Traditional methods, often characterized by low accuracy and high resource consumption, severely limit effective farming decision-making and sustainability. In response to these challenges. A proposed method is introducing the Optimized Crop Prediction and Monitoring Model (OCPMM), a groundbreaking approach that harnesses the power of advanced Machine Learning Algorithms (MLAs) to elevate accuracy and efficiency in crop management. The OCPMM distinguishes itself through innovative features such as real-time data analysis and predictive analytics, establishing a new benchmark for precision agriculture. Our comparative analysis reveals that the OCPMM markedly surpasses conventional methods, such as the Standard Crop Yield Estimation Technique (SCYET), across key metrics, including prediction accuracy, resource utilization, and time efficiency. These enhancements are instrumental in optimizing crop yields, minimizing waste, and advancing sustainable agricultural practices. By rigorously addressing the deficiencies of traditional systems and highlighting the superiority of the OCPMM with empirical evidence, this study illuminates the transformative potential of machine learning technologies in agriculture. It presents a scalable, reliable, and efficient framework poised to revolutionize agricultural practices, paving the way for a future where farming decisions are informed by precision and foresight. © 2024 IEEE.

关键词： Adversarial machine learning

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Object Detection Framework Using OpenCV for Low Cost and High Performance 11th

Object Detection Framework Using OpenCV for Low Cost and Hig...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Bhardwaj, Sarthak Soni, Pratyaksh Ul Haq, Furqan Masih, Alvin Precious Sharma, Puneet Dept. of Computer Science & Engineering Amity University Uttar Pradesh Noida India School of Computer Science & AI SR University Telangana Warangal India

ISBN: (纸本)9789819717231

In contemporary times, advancements in technology have led to a renewed interest in the development of autonomous vehicles, commonly referred to as self-driving cars. These vehicles are equipped with sensors and computer vision tools that enable them to detect and respond to their environment, reducing the need for human intervention in the navigation process. Given the transformative potential of this technology, there has been a growing emphasis on researching and developing autonomous vehicles as a way of revolutionizing the transportation industry. The goal of this research work is to code highly optimized plus parameterized Python algorithms that would drive the object detection systems like Canny edge, pedestrian, vehicle, etc., with the help of pre-existing object models with greater emphasis on reducing the cost-performance trade-off. Various object models like EfficientNet, YOLOv4, R-CNN, etc. were deployed and tested for creating comparison in terms of ease of deployment, efficiency, accuracy, computational, and hardware costs to filter out the most efficient model for the coded computer vision tools. These innovative systems will possess the capability to detect lane markings, accurately identify pedestrians and other vehicles, and continually monitor the vehicle's immediate surroundings, thereby providing real-time information to the autonomous driving system through highly customizable parameters that can be changed according to the needs. By default, the codes in the research work will be customized for Indian roads, which are known for their unique challenges, such as lack of lane markings, heavy traffic, and poor road conditions. However, challenges remain, including legal and regulatory issues, code robustness, hardware integration, public perception, acceptance, and ensuring the safety and security of the autonomous driving system. Despite these challenges, the potential of autonomous vehicles to revolutionize transportation is significant. © The A

关键词： Autonomous vehicles

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Priority-Considered Channel Access for Registration-Backoff-Time (RBT) -based IEEE 802.11ah IOT Network 17

Priority-Considered Channel Access for Registration-Backoff-...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Huang, Chung-Ming Chen, Chia-Hsiang Ma, Jianhua National Cheng Kung University Dept. of Computer Science and Information Engineering Tainan Taiwan Hosei University Faculty of Computer and Information Science Tokyo Japan

ISBN: (纸本)9798350351637

This work devises a High Priority Reservation - centric Registered Backoff Time -based (HPR-RBT) channel access method to enhance the performance of IEEE 802.11ah IoT network. The proposed HPR-RBT method allows the STAs that have high priority's uplinked data to reserve, which is through the claiming action, the privilege of channel access. HPR-RBT adopts the Trigger RAW mode of IEEE 802.11ah and the RBT mechanism. In HPR-RBT, the Restricted Access Window (RAW) of IEEE 802.11ah is divided into (1) a claiming RAW that contains multiple claiming slots and (2) a data RAW that contains multiple data slots. Each claiming slot is further divided into many tiny slots, each of which is for one STA to claim its channel access's reservation for its high priority's uplinked data. Each data slot is further divided into a scheduled sub-slot, in which the channel access of the high priority's uplinked data is scheduled, and an unscheduled sub-slot, in which the channel access of the regular priority's uplinked data is allowed. Furthermore, this work proposes a grouping mechanism to balance the load of high priority's uplinked data across RAW's data slots to prevent excessive high priority's uplinked data in any particular RAW's data slot. Through the grouping mechanism, the IEEE 802.11ah Access Point (AP) can assign the data slot that has the lowest load of high priority's uplinked data to a newly associated STA of the AP. Comparing with the legacy 802.11ah method, the experiment result shows that the proposed HPR-RBT method has (i) the shorter transmission latency and (ii) the better throughput when the number of STA becomes bigger. © 2024 IEEE.

关键词： Internet of things

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Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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An Improved Diagnostic Approach for Classifying Tomato Leaf Diseases using Ensemble Deep Learning based Technique 8

An Improved Diagnostic Approach for Classifying Tomato Leaf ...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Batra, Salil Chakraborty, Gouri Shankar Bortty, Joy Chakra Polok, Sourav Das Ahmed, Tamjid Taki, Tanvir Ahmed Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Westcliff University Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350367904

Tomato is one of the most common vegetables that has high commercial importance for abundant production to meet the huge demand world-wide. There are several challenges that put impact on the mass production of tomato, and which are required to be resolved timely. One of the major challenges is tomato leaf diseases where different types of diseases are there which need to be identified correctly to protect the plants, otherwise it can cause severe harm to the plants which ultimately effects the production of tomato. To have a solution, several scientists and researchers have been working on this to classify diseases in a very initial stage. Diseases of the plants can be classified from leaf image, but it's difficult to identify disease manually by observing through eyes. Deep learning techniques are being used to identify different types of disease using the leaf image. Several existing approaches have been discussed in this study where different detection processes have been proposed. But, in most of the cases, several limitations have been observed regarding the performance of those traditional frameworks. In this study, a novel disease detection approach has been proposed to detect multiple types of tomato leaf disease through analyzing the leaf images. Here, after combing the performances of four CNN models (MobileNetv2, ResNet50, VGG19, VGG16), an ensemble deep learning technique has been applied to identify different types of the tomato leaf disease as well as to classify the healthy and affected leaf. The performance achieved from the proposed ensemble framework ensures optimum accuracy of 96.7% with efficiency. A proper comparative analysis has been presented that supports the novelty and potentiality of the work. © 2024 IEEE.

关键词： Fruits

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A Novel Deep Learning Based Approach for Detecting and Localization of Pulmonary Cancer 14

A Novel Deep Learning Based Approach for Detecting and Local...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek Manoj, Diwakar Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

In the ever-evolving field of scientific diagnostics, the early diagnosis of pulmonary most cancers continues a key undertaking. This observation proposed a unique deep learning-primarily based approach, in particular using Generative Adversarial Networks (GANs), intending to modernise the identification and localization of pulmonary malignancies through scientific imaging. Our models, trained using a varied dataset, demonstrated a promising accuracy fee of 70% within the sample set, suggesting its ability to adeptly differentiate between malignant and non-malignant instances in scientific images. While the conclusions suggest a significant growth in lung cancer detection, also they highlight locations demanding in addition refinement. The balance of technological prowess and scientific significance, as reflected through criteria like sensitivity and specificity, remains a focus topic for future projects. The outcomes of this research are substantial. Beyond the on the spot discoveries, the take a look at emphasizes the transformational possibility of incorporating sophisticated AI approaches into healthcare. As the scientific network grapples with the difficulties of early cancer identification, gear like the one displayed in this study ought to usher in a new age in diagnostics-marked by accuracy, efficiency, and patient-centricity. In conclusion, this have a look at now not simplest adds a fresh diagnostic tool to the sector but moreover sets the way for future innovations within the confluence of AI and healthcare. © 2024 IEEE.

关键词： Generative adversarial networks

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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

NeuroNet: An Attention-Driven Lightweight Deep Learning Mode...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Istyak Nabil, Hadiur Rahman Abir, Golam Rabbani Hossain, Tazdik Das, Aritra Mridha, M.F. American International University-Bangladesh Dept. of Computer Science Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI Dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet's architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet's potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging. © 2024 IEEE.

关键词： Diseases

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