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LIME Explainability on Flower Classification 6

LIME Explainability on Flower Classification

6th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2022

作者： Nikith, Boda Venkata Nikhil, Masabattula Teja Sri Siddhartha, Mutyala Sai Murali, K. Amrita Vishwa Vidyapeetham Dept. Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Dept. Mathematics Bengaluru India

ISBN: (纸本)9781665456982

Artificial Intelligence is becoming more advanced with increasing complexity in generating the predictions and as a result it is becoming more challenging for the users to understand and retrace how the algorithm is predicting the outcomes. Artificial intelligence has also been contributing in making decisions. There are many flowers in the world so the botanist scientists need help in identifying or recognizing which type of flower. The paper presents an x-ray diagnostic model and the explained with Local interpretable model-agnostic explanations LIME method. The model is trained with various COVID as well as non-COVID images. Whereas chest X-rays are segmented to extract the lungs and the model predictions are tested with perturbated images that are generated using LIME. This paper opens a wide area of research in the field of XAI. © 2022 IEEE.

关键词： Lime

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Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API

Time-Dependent Multi-Objective Trip Planning by Ant Colony O...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Etsushi Saeki Siya Bao Toshinori Takayama Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University ZENRIN DataCom

In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction.

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GNN-based Disease Detection and Classification in Cassava Leaf

GNN-based Disease Detection and Classification in Cassava Le...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Meenalochini A Amalroshini K L Megha Nandhini C R Sogil Murugan Dept. of Computer Science and Design Kongu Engineering College Erode India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases.

关键词： Accuracy Scalability Feature extraction Graph neural networks Real-time systems Convolutional neural networks Protection Intelligent systems Diseases Farming

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Flying Vines: Design, Modeling, and Control of a Soft Aerial Robotic Arm

arXiv

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arXiv 2025年

作者： Jitosho, Rianna Winston, Crystal E. Yang, Shengan Li, Jinxin Ahlquist, Maxwell Woehrle, Nicholas John Liu, C. Karen Okamura, Allison M. Dept. of Computer Science Dept. of Mechanical Engineering Stanford University StanfordCA94305 United States

Aerial robotic arms aim to enable inspection and environment interaction in otherwise hard-to-reach areas from the air. However, many aerial manipulators feature bulky or heavy robot manipulators mounted to large, high-payload aerial vehicles. Instead, we propose an aerial robotic arm with low mass and a small stowed configuration called a "flying vine". The flying vine consists of a small, maneuverable quadrotor equipped with a soft, growing, inflated beam as the arm. This soft robot arm is underactuated, and positioning of the end effector is achieved by controlling the coupled quadrotor-vine dynamics. In this work, we present the flying vine design and a modeling and control framework for tracking desired end effector trajectories. The dynamic model leverages data-driven modeling methods and introduces bilinear interpolation to account for time-varying dynamic parameters. We use trajectory optimization to plan quadrotor controls that produce desired end effector motions. Experimental results on a physical prototype demonstrate that our framework enables the flying vine to perform high-speed end effector tracking, laying a foundation for performing dynamic maneuvers with soft aerial manipulators. Copyright © 2025, The Authors. All rights reserved.

关键词： End effectors

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Monarch Butterfly Optimization based Multi-Convolution Neural Network for Lung Cancer Classification 2

Monarch Butterfly Optimization based Multi-Convolution Neura...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Uma Maheswari, S. Adnan, Myasar Mundher Lathakumari, K.R. Pradeep, V. Praveena, Hirald Dwaraka Karunya Institute of Technology and Sciences Data Science and Cyber Security Coimbatore India The Islamic University Department of Computer Science Najaf Iraq Nitte Meenakshi Institute of Technology Dept of Ece Bengaluru India Alva's Institute of Engineering and Technology Department of Information Science and Engineering Mangalore India School of Engineering Mohan Babu University Erstwhile Sree Vidya Nikethan Engineering College Department of Ece Andhra Pradesh India

ISBN: (纸本)9798350382952

The lung cancer generally presented a pulmonary nodule on images of diagnostic and correct estimation in malignant pulmonary nodules is difficult for diagnosis and protecting of lung cancer. However, the existing methods has less classification accuracy because of irrelevant features on classification of lung cancer. In this research, proposed a Monarch Butterfly Optimization based Multi-Convolution Neural Network (MBO based M-CNN) method for lung cancer classification. dataset used for lung cancer classification is LIDC-IDRI dataset and it is pre-processed by Histogram Equalization (HE). Then, the features are extracted by Gray Level Co-occurrence Matrix (GLCM) and extracted features are selected by Monarch Butterfly Optimization (MBO) algorithm. Then, the lung cancer classification is performed by Multi-Convolution Neural Network (M-CNN). The performance of proposed algorithm is tested in terms of accuracy, precision, sensitivity and specificity. Developed technique obtained accuracy 99.71%, precision 95.39%, sensitivity 99.46%, f1-score 97.45% that is better than previous method like Adaptive Boosting Self-Normalized Multiview Convolution Neural Network (AdaBoost-SNMV-CNN). © 2024 IEEE.

关键词： Adaptive boosting

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Futuristic Authentication: Development of an Artificial Intelligence based Enhanced Face Detection and Recognition System 7

Futuristic Authentication: Development of an Artificial Inte...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rama Satish, K.V. Jadhav, Dattatraya Arun Siva Prasad, M.V. Gopal Thatipudi, Jayendra Tejasri, V. Divya, J. Rnsit Dept of Ai Ml Bengaluru India Keystone School of Engineering Electronic And Telecommunication Department Pune India Anurag Engineering College Department of Cse Kodad India University of North Texas Dept. of Computer Science India S.R.K.R.Engineering College Department of It Chinaamiram Andhra Pradesh Bhimavaram India R.M.K.Enginnering College Dept of Computer Science And Design India

ISBN: (纸本)9798350359299

This research presents the design and implementation of an advanced Face Detection and Recognition System employing an Artificial Intelligence Assisted Face Recognition Model (AIFRM). The proposed system leverages the powerful Cascaded VGG16 architecture to enhance the accuracy of face detection and recognition tasks. The methodology involves a two-step approach, beginning with robust face detection using the cascaded VGG16 model. This is followed by feature extraction and recognition using the same architecture, resulting in a highly efficient and accurate facial recognition system. The cascaded VGG16 model is trained on diverse datasets to ensure adaptability to various scenarios and lighting conditions, enhancing the system's real-world applicability. The experimental results demonstrate the effectiveness of the proposed system in achieving state-of-the-art accuracy in face detection and recognition. The cascaded VGG16 model proves to be a formidable backbone for the AIFRM, contributing to the system's success in handling complex scenarios. The system's high accuracy, reaching 97%, positions it as a reliable solution for security, surveillance, and authentication applications in diverse environments. © 2024 IEEE.

关键词： Face recognition

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EVALUATION OF PERFORMANCE OF A CI ENGINE FUELED WITH BIODIESEL PRODUCED FROM UNUSED ALGAE

Journal of Solid Waste Technology and Management

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Journal of Solid Waste Technology and Management 2023年第4期49卷 359-364页

作者： Karmakar, Rachan Kumar, Nitin Rajor, Anita Kundu, Krishnendu Kumar, Rajesh Gahlot, Dinesh Chaudhury, Bhaskerpratap Ragit, S.S. Kumar, Amit Gorai, Sanjoy Gangwar, Pavan SInghal, Rishabh Bhattacharya, Sourish Sharma, Rachna Savita Singh, Narpinder Kumar, Neeraj Graphic Era Deemed to be University Uttarakhand Dehradun India Dept. of Mechanical Engineering Roorkee Institute of Technology Uttarakhand Roorkee India School of Energy and Environment Thapar University Punjab Patiala India Dept. of Biofuel CSIR CMERI CoEFM Punjab Ludhiana India Dept. of Chemistry S.S.J. University Campus Almora Uttarakhand India Dept. of Physics Chandigarh Group of Colleges Punjab Jhanjheri India Dept. of Mechanical Engineering Guru Nanak Institute of Technology Maharashtra Nagpur India Dept. of Computer Application Quantum University Uttarakhand Roorkee India Dept. of Electronics and Communication Engineering IMS Engineering College U.P. Gaziabad India Dept. of Electrical Engineering COER University Uttarakhand Roorkee India Process Design and Engineering Cell Discipline CSIR Central Salt and Marine Chemicals Research Institute Gujrat Bhavnagar India Dept. of Applied Science Roorkee Institute of Technology Uttarakhand Roorkee India School of Agriculture Graphic Era Hill University Uttarakhand Dehradun India Dept. of Food Science and Technology Graphic Era Deemed to be University Uttarakhand Dehradun India

Abundant availability and easy culture process of algae make it a better resource than other vegetable crops for biodiesel production. The oil (with 21% FFA content) extracted from the unused, mixed culture of algae in this experiment was used to produce biodiesel by an ‘acid esterification followed by alkaline esterification’ procedure. After confirming the properties of the biodiesel to be within the limit of ASTM standard, three biodiesel blends (B10, B20 and B30) were used in an internal combustion engine (Four stroke single cylinder VCR engine) and the performance of the engine was observed at different engine loads (0%, 20%, 40%, 80%, 100%, 120%). The little higher brake specific fuel consumption (0.22kg/KWh, 0.25 kg/KWh, 0.26kg/KWh and 0.21kg/KWh respectively for B10, B20, B30 and petro-diesel at overload condition), lower brake power (3.41 kW, 3.37 kW, 3.25 kW, 3.39 kW for diesel, B10, B20 and B30 respectively for B10, B20, B30 and petro-diesel) and mechanical efficiency (63.34, 51.43%, 52.06% and 51.43% for petro-diesel, B10, B20 and B30 respectively) for biodiesel blends took place which might be the results of lower calorific value (40800 kJ/kg), higher density (875.27kg/m3) and viscosity (3.14 mm2/s) of the algal biodiesel than diesel. © 2023 Widener University School of Civil engineering. All rights reserved.

关键词： Biodiesel

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Field Demonstration of Blockchain-based Security for a Solar Farm

Field Demonstration of Blockchain-based Security for a Solar...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： BoHyun Ahn Kalyan Nakka Nathanial R. Handke Trevor J. Reyna Taesic Kim Dept. of Electrical Engineering and Computer Science Texas A&M University-Kingsville Kingsville TX USA Dept. of Computer Science and Engineering Texas A&M University College Station TX USA Dept. of Electrical Engineering and Computer Science University of Missouri-Columbia Columbia MO USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper introduces a Blockchain (BC)-based security model for a solar farm, providing security functions such as firmware patching management, role-based access control, public key infrastructure, and man-in-the-middle attacks detection (MITM), malware file detection. In particular, this paper provides detailed field-testing methods at a solar fam to demonstrate the feasibility and effectiveness of cyber-attack detection methods. Practical cyberattacks targeting solar farms are designed and conducted. It is demonstrated that the proposed BC-based system proactively detects MITM attacks, firmware modification attack, and malware attack, while ensuring the continuous operation of the solar farm during attack events.

关键词： Solar farms Solar energy Network security Malware Vectors Blockchains Security Testing Payloads Microprogramming

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Performance Analysis of Slicing on a 10-node 5G Architecture for Networked Music Performances

Performance Analysis of Slicing on a 10-node 5G Architecture...

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IEEE Symposium on computers and Communications (ISCC)

作者： Luca Turchet Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Networked Music Performances (NMPs), where geographically displaced musicians play together over a data network, represent a challenging application for today’s wireless communications. This is due to the stringent constraints on latency, throughput, and reliability that need to be obeyed in order to achieve a satisfactory quality of experience for the musicians. Slicing is a promising feature of 5G networks in the context of NMP applications, as it makes it possible to isolate the networking and computing resources allocated to NMP devices. However, the use of slicing has been scarcely investigated for the NMP context so far. Moreover, previous works focusing on NMPs over 5G involved up to 4 nodes. To bridge these gaps, we study 5G performance in support of NMPs involving an architecture with 10 nodes, both with and without slicing. Specifically, we focused on the assessment of the sole wireless link, as the measurements can be easily transferred to a realistic NMP architecture involving a wide area network. Our results show that, in the slicing condition, latency slightly increased due to the realistically different computing specifications of the MEC servers compared to those of the Core Network servers, whereas reliability slightly improved as expected.

关键词： Wireless communication Wide area networks 5G mobile communication computer network reliability Music computer architecture Throughput Reliability Servers Quality of experience

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Digital Makerspace - A Tool with Multipurpose Use and Potential for Teaching and Others 14

Digital Makerspace - A Tool with Multipurpose Use and Potent...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Rothe, Irene Kless, Andre Bonn-Rhein-Sieg University of Applied Sciences Dept. of Electrical Engineering Mechanical Engineering and Technical Journalism Sankt Augustin Germany Bonn-Rhein-Sieg University of Applied Sciences Dept. of Computer Science Sankt Augustin Germany

ISBN: (纸本)9798350399431

A multipurpose software tool for building web apps and software-based Open Education Resources, called 'Digital Makerspace' (DMS), was developed in the last 8 years at the applied university and is now ready for use. The tool is useful for creating and designing almost all thinkable material and tools for online usage by web users, for example providing digital learning material and digital interactive exercises. The tool is free for use for everyone and is supposed to be self-explaining. In this paper, the DMS is introduced and as an example multi-purpose learning material for students are shown which were designed via the DMS. © 2023 IEEE.

关键词： E-learning

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