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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Shanthamalar, J. Jeslin Nithya, S. Kumar, Alle Nithin Chandra, Beraboina Jaya Nancy Noella, R.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9789819788644

In the contemporary corporate landscape, efficient utilization of time is paramount for organizational success. Business meetings, a fundamental component of decision-making processes, often result in copious amounts of information exchange. This project aims to streamline and enhance the post-meeting experience through the development of an Automated Business Meeting Summarization system. Additionally, the system employs sentiment analysis to gauge the overall tone and mood of the meeting, providing valuable insights into team dynamics and engagement levels. The project aims to empower organizations with an efficient tool that not only accelerates the decision-making process but also fosters better collaboration and understanding among team members. In contrast, zero-shot Whisper models demonstrate a robustness frontier that includes the 95% confidence interval for human performance. The BART-large-CNN-SAMSum model demonstrates promising performance on the SAMSum summarization dataset, achieving a ROGUE-1 score of 42.621. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sentiment analysis

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Smart Home Gardening with Robotic Assistance using Cloud-Connected Plant Care and Neural Networks Growth Analysis 1

Smart Home Gardening with Robotic Assistance using Cloud-Con...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Rajalakshmi, P. Dhivya, K. Anitha, D. Sandhya, Potturi Gnanavel, N. Muthulekshmi, M. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tami Nadu Chennai India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Information Technology Tamil Nadu Namakkal India Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376517

This research introduces a new method for home gardening that uses neural networks to analyze plant development, cloud-connected plant care, and robotic help. The goal of combining these technologies is to make home gardening more efficient and productive while also allowing for remote management and monitoring. To understand how plants develop, our system collects data in real-time from a variety of sensors that measure things like temperature, humidity, and light levels, and then runs that data through neural networks. To further alleviate homeowners' workloads and guarantee plants get ideal care, robotic assistance is used to automate activities like watering, fertilizing, and pest management. Connecting to clouds allows customers to manage their garden from anywhere, get alerts when certain conditions are met, and get maintenance advice based on data. Through a series of battery tests, it can be observed that the proposed technique is superior to conventional gardening practices in terms of plant development and health. For eco-conscious green thumbs and busy gardeners, our system is the next big thing in smart home gardening, providing a practical and long-term solution to their problems. © 2024 IEEE.

关键词： Automation Plant health Remote monitoring Robotic assistance Sustainable gardening

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A Survey on Different Methods for Resource Allocation in Internet of Things 2

A Survey on Different Methods for Resource Allocation in Int...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Sasikala, B. Kalaiselvi, K. Murugan, V. Senthil Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India Faculty of Engineering and Technology Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350347579

The progressive computational era has witnessed the Internet of Things (IoT) the implementation of an automation-based superior quality eco-system, new and reliable services, and greater productivity are receiving a growing amount of attention. This is the reason behind IoT currently expanding over a vast array of applications such as in the academic, healthcare, agricultural, military, industrial fields, etc. Storage, processor, network, sensor nodes, energy, and other IoT resource demands are varied in those applications. One apparent concern is the means of managing these constrained resources optimally. Lately, it has been strived hard to achieve an optimal resource management applying different scheduling approaches. In this research the various techniques utilized for IoT resource scheduling is presented. In addition, it is analyzed, and their strengths and weaknesses are compared. The survey also provides a study based on various metrics of IoT resource scheduling and besides this, various resource scheduling approaches are compared. At last, it is believed that much scope exists for the improvement of IoT resource management retaining the overall quality required. In this research, this kind of future direction of research in this domain is considered. © 2023 IEEE.

关键词： Internet of things

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Classification of Autism Spectrum Disorder using CNN & Transfer Learning 2

Classification of Autism Spectrum Disorder using CNN & Trans...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Shyamali Chaudhury, Pamela Tripathy, Hrudaya Kumar Sillicon Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9798350370188

Autism spectrum disorder (ASD) is a neurodevelopment condition that impacts a person's ability to communicate and interact socially. Diagnosing autism spectrum disorder (ASD) early and correctly is critical for providing appropriate help to those who need it. Transferring information or learned representations from one task or domain to another is possible using a computerized learning technique known as transfer learning. In this study, we proposed a method for classifying ASD using an Enhanced Layerwise Sequential Convolutional Neural Network (ELS-CNN) with transfer learning. Transfer learning involves leveraging pre-trained models on large-scale datasets used for the Kaggle platform and fine-tuning them for specific tasks. A dataset that tested these models was collected and consisted of 2,928 face images. These images are preprocessed using Adaptive Median Filtering to remove noise from an image and Histogram Equalization to improve image contrast. Otsu's Binary Threshold can be used for image segmentation. Extracting the features can be Principal Component Analysis (PCA) for image compression. We suggested the ELS-CNN algorithm, which has demonstrated good performance in a number of image identification applications. The output of the deep learning neural network models was assessed using common assessment measures like accuracy, precision, recall, and fl-score. The Enhanced Layerwise Sequential CNN algorithm achieved the highest accuracy. © 2024 IEEE.

关键词： Principal component analysis

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Cluster Based Concurrent Data Collection with Fault Tolerance for Shared IoT Applications 16

Cluster Based Concurrent Data Collection with Fault Toleranc...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kumar, Arvind Matam, Rakesh Swain, Srinibas Mukherjee, Mithun Indian Institute of Information Technology Guwahati Department of Computer Science and Engineering India Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350383119

Sharing IoT device infrastructure by multiple entities or applications for their respective sensing and monitoring needs is an effective way to reduce the upfront setup cost, ownership costs, and also to prevent over provisioning. Concurrent data collection in such a shared environment is a major challenge, given the number of devices and multiple processes relying on that data. Most of the existing works in this direction typically consider network scenarios where devices can reach the base station(s) directly. As this assumption is not always valid, few other works focused on concurrent data collection over multiple-hops. In such a shared multi-hop environment, the chances of device failures are high, especially considering the power constraints of devices. To address the issue of reliability in data collection, this paper presents a cluster-based fault tolerant network structure for concurrent data collection. The proposed work aims to minimize the delay and handles the topology changes in case of faults, and also lowers the transmission count. The validity of the proposed work is shown via analytical and simulation results. © 2024 IEEE.

关键词： Concurrent Data collection in IoT Network topology Shared IoT device infrastructure

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Revolutionizing Scene Text Recognition: Unleashing the Power of Dual Step Attention Mechanism in the Encoder 6th

Revolutionizing Scene Text Recognition: Unleashing the Power...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Selvam, Prabu Sumathi, M. Maniappan, Varun Padmavathi, A. Natarajan, B. Syed Husain, S. Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India School of Computing SASTRA Deemed University Thanjavur India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai601103 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Engineering Tiruchirappalli India

ISBN: (纸本)9789819734412

Text recognition in natural scenes presents a significant challenge in computer vision despite its widespread applications in real-life scenarios. Deep learning advancements have notably enhanced the precision of scene text recognition. The prevailing framework for scene text recognition currently revolves around the encoder–decoder architecture, with Connectionist Temporal Classification (CTC) and attention mechanisms serving as popular text recognition techniques. However, existing methods rooted in the encoder–decoder architecture primarily integrate the attention mechanism into the decoder, limiting their capacity to identify characters accurately. Conversely, CTC-based methods exhibit faster processing times and impose fewer computational burdens, yet they are comparatively less effective than their attention-based counterparts. We introduce a new text recognition network based on a transformer-based network to address these limitations. Our proposed network introduces the Dual Step Attention Mechanism (DSAM), wherein the encoder's attention mechanism captures the overall text location within the image. In contrast, the decoder's attention mechanism precisely identifies the location of each character in the word image. In addition, we have employed a bidirectional encoding to capture the capture character sequence in both directions. To validate the efficacy of our approach, we conducted a comprehensive performance evaluation comparing our proposed network with state-of-the-art methods across six diverse text datasets, namely IIIT5K, SVT, IC13, IC15, SVTP, and CUTE. The results indicate that our model outperforms existing approaches across various metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Detection of Wormhole Attacks Using the DCNNBiLSTM Model to Secure the MANET 3rd

Detection of Wormhole Attacks Using the DCNNBiLSTM Model to ...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Rajalakshmi, B. Anandhi, R.J. Moorthi, K. Kavin, Balasubramanian Prabhu Dhanaraj, Rajesh Kumar New Horizon College of Engineering Ring Road Bellandur Post Bengaluru560103 India Department of Computational Intelligence College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Department of Data Science and Business Systems College of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur603203 India Pune India

ISBN: (纸本)9789819708918

The obvious response to an anytime, anywhere network in the current era of heterogeneous networks is MANET. It is a collection of wirelessly communicative mobile gadgets that operate on their own. In order to stop the damage that rogue network nodes could do, security in MANET is a crucial duty. A category of denial-of-service (DoS) assault, the wormhole attack bombards the network with numerous phoney packets and messages in an effort to exhaust its resources. Wormhole attacks are a particular type of network layer attack that imitates routing algorithms. Because of deep learning's exceptional performance in many detection and identification tasks, this research presents an approach for intelligent and effective attack detection in MANET. Our goal is to mimic a wormhole attack in a network environment with many wormhole tunnels. The CNN layer is used by the DCNNBiLSTM architecture to extract features from input data, and BiLSTMs are used to predict sequences. Five performance indicators were used to compare CNN, LSTM and DCNNBiLSTM under wormhole attack. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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FORCE: Optimal Resource Utilization Framework for Fog-enabled Green Communication in IoMT 18

FORCE: Optimal Resource Utilization Framework for Fog-enable...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Patel, Kaju Kanjia, Angel Kumhar, Malaram Bhatia, Jitendra Jadav, Nilesh Kumar Guptall, Rajesh Tanwar, Sudeep Gupta, Ankur Gujarat Technological University Ahmedabad382424 India Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India Model Institute of Engineering & Technology J&k Department of Computer Science & Engineering India

ISBN: (纸本)9798350391725

The Internet of Medical Things (IoMT) has revolutionized healthcare by integrating smart devices to monitor, analyze, and optimize patient care. Various healthcare devices generate a large volume of data daily, which uses a lot of storage and bandwidth. Fog computing is a promising paradigm that extends cloud computing capabilities near the network edge, allowing for real-time processing of healthcare data. In a fog-enabled IoMT environment, effective load balancing is essential to maximize resource utilization and guarantee timely and reliable healthcare services. This paper presents a load-balancing framework, i.e., FORCE, especially for fog-enabled IoMT environments. The FORCE focuses on challenges present in healthcare applications, such as meeting low latency requirements and dynamic workloads, and thus accelerates the idea of faster communication between the end devices, or IoT wearables, and fog nodes. To provide low-latency processing, the framework considers the fog computing architecture and brings computation close to healthcare devices. The FORCE delivers healthcare services with reduced latency and energy consumption. For the scenario wherein the region attribute is used to determine the optimal node result, the time to compute that was obtained is 14.82 seconds. In contrast, the computation time in later scenarios without considering region attributes is {7 6. 8 9} seconds. Hence, the FORCE efficiently reduces the searching time for finding the optimal node. Based on the simulation results, it can be concluded that the FORCE effectively optimizes both latency and energy consumption. © 2024 IEEE.

关键词： Fog computing

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Mapping of City Development and Urban Flooding Using Deep Learning Model for Chennai City Study Area 5th

Mapping of City Development and Urban Flooding Using Deep Le...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Senthilkumar, Pavithraa Arthur, Menaka Pushpa School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai600127 India

ISBN: (纸本)9789819747108

People’s lives and property are put in jeopardy by extreme weather occurrences such as floods, but key infrastructures, which must work even under subpar conditions, are also severely damaged. There have been a number of attempts to use deep learning algorithms to map the urban flood regions. In order to determine how vulnerable an area is to catastrophic flooding, it is critical to evaluate the flood-prone zones. Using Sentinel satellite photos, along with deep learning model, can help to map flood-prone areas and assess their vulnerability. Flood hazard mapping was evaluated using a Convolutional Neural Network (CNN) model. The response variable was a flood inventory of Chennai Corporation’s flooded and non-flooded areas, while the predictor variables were flood-affecting factors. The flooded areas were then randomly divided into sections for the purpose of constructing flood models and testing them. According to the training and testing classification accuracy, the models were evaluated on their ability to predict outcomes. Flood protection and risk assessment might benefit from the use of the model. As a result, if a flood inventory is available, this method might be used to create a flood danger map for metropolitan areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Risk assessment

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