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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： P Chitra K Sridar Sudha Govindan Britto Raj S Akshya. J Mani Deepak Choudhry M. Sundarrajan Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Department of Computer Science and Engineering Veerammal Engineering College Dindigul Department of Computer Science and Engineering Sona College of Technology Salem Department of Computer Science and Engineering RRASE College Of Engineering Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems.

关键词： Navigation Statistical analysis Machine learning Generative adversarial networks Path planning Real-time systems Generators Safety computational efficiency Autonomous robots

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Real-Time Human Action Recognition with Multimodal Dataset: A Study Review 5th

Real-Time Human Action Recognition with Multimodal Dataset: ...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Joshi, Kapil Rastogi, Ritesh Joshi, Pooja Anandaram, Harishchander Gupta, Ashulekha Mohialden, Yasmin Makki Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of IT Noida Institute of Engineering and Technology Greater Noida India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Dehradun India Computer Science Department Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9789819906000

Due to difficulties including a cluttered background, partial occlusion, and variations on dimensions, angle, illumination, or look, identifying human endeavors using video clips or still images is a challenging process. A numerous mechanism for recognizing activity is necessary for numerous applications, such as robotics, human–computer interaction, and video surveillance for characterizing human behavior. We outline a classification of human endeavor approaches and go through their benefits and drawbacks. In specifically, we classify categorization of human activity approaches into the two broad categories based on whether or not they make use of information from several modalities. This study covered a depth motion map-based approach to human recognizing an action. A motion map in depth created by adding up to the fullest differences with respect to the two following projections maps for each projection view over the course of the entire depth video series. The suggested approach is demonstrated to be computationally effective, enabling real-time operation. Results of the recognition using the dataset for Microsoft Research Action3D show that our method outperforms other methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Risk-Aware Security-Constrained Unit Commitment: Taming the Curse of Real-Time Volatility and Consumer Exposure

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第4期2卷 536-551页

作者： Bienstock, Daniel Dvorkin, Yury Guo, Cheng Mieth, Robert Wang, Jiayi Columbia University Department of Industrial Engineering and Operations Research New YorkNY10027 United States Johns Hopkins University Department of Electrical and Computer Engineering Department of Civil and System Engineering Ralph O'Connor Sustainable Energy Institute BaltimoreMD21218 United States Clemson University School of Mathematical and Statistical Sciences ClemsonSC29634 United States Rutgers University Department of Industrial and Systems Engineering New BrunswickNJ08901 United States Stanford University Department of Management Science and Engineering StanfordCA94305 United States

We propose an enhancement to wholesale electricity markets to contain the exposure of consumers to increasingly large and volatile consumer payments arising as a byproduct of volatile real-time net loads - i.e., loads minus renewable outputs - and prices, both compared to day-ahead cleared values. We incorporate a trade-off, motivated by portfolio optimization methods, between standard day-ahead payments and a robust estimate of such excess payments into the day-ahead computation and specifically seek to account for volatility in real-time net loads and renewable generation. Our model features a data-driven uncertainty set based on principal component analysis, which accommodates both load and wind production volatility and captures locational correlation of uncertain data. To solve the model more efficiently, we develop a decomposition algorithm that can handle nonconvex subproblems. Our extensive experiments on a realistic NYISO data set show that the risk-aware model protects the consumers from potential high costs caused by adverse circumstances. © 2024 IEEE.

关键词： Principal component analysis

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Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Wen Gu, Yanmei Wang, Zhiming Zhu, Huijia Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Shanghai China Ant Group Shanghai China

ISBN: (纸本)9798350368741

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods. © 2025 IEEE.

关键词： anti-spoofing audio deepfake detection generalization

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IoT Alert Reflexion of Forbidden Deforestation Regions with Drone observation 3

IoT Alert Reflexion of Forbidden Deforestation Regions with ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Saravanan, S.K. Krishna Kumar, T.P. Udaya Suriya Rajkumar, D. Krishnamoorthy, R. Narayana Rao, R. Thiagarajan, R. Srm Valliammai Engineering College Department of Computer Science Kattangulathur India Nehru School of Management@ Nehru College of Engineering and Research Centre Thrissur India Global Institute of Engineering and Technology Tamilnadu India Chennai Institute of Technology Centre for Computational Modeling Chennai India Vemu Institute of Technology AP India Prathyusha Engineering College Department of Cse Chennai India

ISBN: (纸本)9781665462167

This article proposes a novel technique to assist the management or an authorized individual to identify the location of the deforestation activities such as tree-cutting, fires, or the intense heat in the forest via GPS, as it is now becoming difficult to stop illegal tree trafficking. IoT can help bots in the establishment of effective forest management and surveillance. The collection and utilization of basic forestry data poses a number of challenges for emerging economies. Data on daily forestry, burned zone evaluation, and forest infraction monitoring can help those in charge of maintaining forests and respond more effectively and make better decisions. © 2023 IEEE.

关键词： Drones

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An Efficient Method for Seeds Classification and Quality Testing Using Fast R-CNN 5

An Efficient Method for Seeds Classification and Quality Tes...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Ashwini, S. Rao, B. Srinivasa Shirgire, Anil Kannan, K.R. Kumar, T Ch Anil Sivashankar, G. School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Dr D y Patil Institute of Technology Maharashtra Pune India Hindusthan Institute of Technology Department of Ece Tamilnadu Coimbatore India Vignan's Foundation for Science Technology and Research Department of Mechanical Engineering Andhra Pradesh Guntur India Srm University Department of Computational Intelligence Tamilnadu Chennai India

ISBN: (纸本)9798350321425

Research into agriculture is gaining an increasing attention in recent years. The newest innovations use a variety of computational technology to make agricultural tasks easier. Seed quality is the most important of many factors that determine agricultural output. Further insights into quality manufacturing, seed quality control, and impurity discovery can be gained by proper seed classification. Seeds have traditionally been sorted into different categories based on their appearance (including their color, texture, and shape). This is often done by experts who must visually inspect each sample, which is a laborious and time-consuming process. Seed sorting is a labor-intensive process that can be greatly improved by automating the inspection process. This study presents a classification system for seeds using Fast RCNN, inspired by the success of computational intelligence methods in other image classification challenges. The suggested method includes five stages: wiener filter preprocessing;HE image enhancement;binarization;adaptive watershed segmentation;and Rapid R-CNN model training. The proposed approach outperforms other two models such as CNN and RNN. © 2023 IEEE.

关键词： Image enhancement

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Towards Context-aware Support for Color Vision Deficiency: An Approach Integrating LLM and AR 13

Towards Context-aware Support for Color Vision Deficiency: A...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Morita, Shogo Zhang, Yan Yamauchi, Takuto Chen, Sinan Li, Jialong Tei, Kenji Tokyo Institute of Technology School of Computing Tokyo Japan University of Tokyo International Research Center for Neurointelligence Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan Kobe University Center of Mathematical and Data Sciences Kobe Japan

ISBN: (纸本)9798350355079

People with color vision deficiency often face challenges in distinguishing colors such as red and green, which can complicate daily tasks and require the use of assistive tools or environmental adjustments. Current support tools mainly focus on presentation-based aids, like the color vision modes found in iPhone accessibility settings. However, offering context-aware support, like indicating the doneness of meat, remains a challenge since task-specific solutions are not cost-effective for all possible scenarios. To address this, our paper proposes an application that provides contextual and autonomous assistance. This application is mainly composed of: (i) an augmented reality interface that efficiently captures context;and (ii) a multi-modal large language model-based reasoner that serves to cognitize the context and then reason about the appropriate support contents. Preliminary user experiments with two color vision deficient users across five different scenarios have demonstrated the effectiveness and universality of our application. © 2024 IEEE.

关键词： Color vision

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A Review of Earth or land surface use and cover Classification using the Landsat8 Remote Sensing data for Dehradun Region

A Review of Earth or land surface use and cover Classificati...

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2023 International Conference on computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Sachin Memoria, Minakshi Kumar, Rajiv Joshi, Kapil Anandaram, Harishchander Gupta, Amit Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Dehradun India Centre for Computational Engineering Networking Amrita School of Artificial Intelligence Coimbatore India Graphic Era Hill University Deptt. of Cse Dehradun India

ISBN: (纸本)9798350323917

In accordance with economic and industrial development, the number of land use or land-cover or (ESU/ESC) deviations has increased tremendously with uncontrolled population growth, particularly in emerging nations in the late 20th and primary 21st periods. In the best efficient way to understand the impact of ESU/ESC and control the effects of land change, ESU/ESC change has become a major concern for ecologists and policymakers to understand how different machine learning classifiers significantly support the estimation of ESU/ESC maps from remote sensing data. Prime purpose of the study is to categorize ESU/ESC applying different machine learning algorithms on the Google Earth Engine platform, specifically using the Random Forest, Support Vector Machine, Classification and Regression Trees to compare the effectiveness of each algorithm using accuracy estimation. Future sustainable development and a knowledge of the dynamics of our changing environment will undoubtedly benefit from ESU/ESC categorization study. © 2023 IEEE.

关键词： Neural networks

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Fine-grained sentiment analysis using neural networks to identify guest preferences based on online reviews 1

Fine-grained sentiment analysis using neural networks to ide...

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1st International Conference on computer, Power and Communications, ICCPC 2022

作者： Rosewelt, L. Antony Kovarasan, Rajesh Kambattan Ponmalar, Sridevi P. Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350397840

Online reviews are a sort of user-generated content that sway not only prospective guests' reservation decisions but also provide timely feedback to hotels on how to enhance their service offerings in response to guest feedback. Customer preferences can be inferred from online hotel evaluations by analysing the reviews for subtle indications of approval or disapproval of specific features. Sentiment component extraction, feature pair identification (FPI), and sentiment orientation analysis are all basic activities in fine-grained sentiment analysis. Yet, current fine-grained sentiment analysis techniques fail to identify FPIs reliably, particularly when dealing with review evaluations. To boost the efficiency of FPI detection, we develop a new convolutional neural network (i.e., CNN) model that can fully exploit unstructured and structured information. In addition, we present a modified fine-grained sentiment analysis methodology that integrates a term clustering algorithm for 'aspect terms' to determine a correct 'customer sentiment intensity value' for each evaluated aspect, rather than a simple 'positive' or 'negative' rating. At last, we undertake an empirical analysis of hotel web reviews to demonstrate the rationality and benefits of the suggested methodology. The empirical findings show that our suggested method can effectively identify consumer preferences from online evaluations of hotels and can enhance the effectiveness of FPI identification. Furthermore, we discover that different sorts of clients have distinct tastes in the amenities offered by hotels. © 2022 IEEE.

关键词： Sentiment analysis

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Navigating the road to sustainable mobility: opportunities and challenges in electric vehicle adoption in Liberia

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Discover Electronics 2025年第1期2卷 1-32页

作者： Eze, Val Hyginus Udoka Tamba II, John Saah Mustafa, Mundu M. Alaneme, George Uwadiegwu Eze, Chidinma Esther Bawor, Hassan Foday Department of Electrical Telecom. & Computer Engineering Kampala International University Kampala Uganda Department of Civil Engineering Kampala International University Kampala Uganda Department of Educational Foundation Kampala International University Kampala Uganda Department of Mathematical Science African Institute for Mathematical Sciences Kigali Rwanda

This study investigates the adoption of electric vehicles (EVs) in Liberia, focusing on the opportunities and challenges associated with this mobility shift. As a developing nation, Liberia faces a peculiar and significant barrier to EV integration, including underdeveloped infrastructure, high importation costs, and a fragile energy grid. By employing a multidisciplinary research design that incorporates a comprehensive PESTLE and SWOT analysis, this study identifies key socio-economic, technological, and regulatory factors influencing EV adoption in Liberia. Additionally, a Technology Adoption Model (TAM) framework is utilized to assess user perceptions and behavioral intentions regarding EVs. The research provides a theoretical contribution to the existing literature on EV adoption in low-income economies by highlighting the unique contextual barriers and enablers specific to Liberia. Unlike previous studies that primarily focus on developed or emerging economies, this paper offers new insights into the role of renewable energy integration specifically hydropower, solar, and biomass in establishing a cleaner and more reliable energy grid to support EVs in Liberia. It also examined the impact of policy frameworks, financial incentives, and public–private partnerships in reducing the economic constraints associated with EV adoption in Liberia. The study underscores how policy reforms, international collaboration, and stakeholder engagement can transform infrastructural challenges into opportunities for sustainable transportation. By addressing issues such as limited rural electrification (currently below 5%) and the transportation sector's reliance on fossil fuels, Liberia can progress toward sustainable mobility and environmental conservation. The findings contribute to the broader discourse on sustainable transportation by providing strategic recommendations for enhancing Liberia’s energy and transportation sectors, emphasizing infrastructure investment, renewabl

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