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2nd international Conference on Mechanical and Energy technologies , iCMEt 2021

作者： Vyas, sonali Narasima Venkatesh, A. Acharjee, Purnendu Bikash Jangali, satish G. singh, Charanjeet Khan, Zarrarahmed Z. School of Computer Science UPES Dehradun Dehradun India Department - Department of HRM and General Management ISBR Business School Karnataka Bangalore India Computer Science The Assam Kaziranga University Rowmarikhuti India Department of Industrial and Production Engineering U.B.D.T. College of Engineering Karnataka Davangere India Electronics and Communication Department Deenbandhu Chhotu Ram University of Science and Technology Harayana Murthal Sonipat India Anjuman I Islam Kalsekar Technical Campus Mumbai University Maharashtra Mumbai India

ISBN: (纸本)9789811901072

the application of internet of things is considered as one of the key cornerstones in the new fifth generation network, it is estimated that more than 40 billion iot devices will be in place around the work by 2025. the current focus of government and non-profit organisations revolves around implementing energy saving practices, emission of carbon and managing the ecology for sustainable future. the basic objective of Green iot is to reduce the emission of harmful greenhouse gases and other toxic pollutants, support the government and other stakeholders in conserving the environment and reduce the power consumption. the adoption of the various techniques through Green iot will lead to creating better and sustainable environment. the Green iot enables in applying efficient procedures which enables in facilitating the reduction of the harmful substances and to enhance sustainable ecology for the future generation. the smart environment is the one which is focused in leveraging the i.t.services, enable in deploying the resources effectively and use the resources so that the energy can be used effectively. this paper attempts to provide the enhanced opportunities and critical challenges in implementing the Green iot towards energy saving practices. the Green iot enables in indicating the application of critical practices for green environment and also supports in saving more cost and energy. this enables in offering systematic solution which will create sustainable growth for the community and also enable in addressing the societal challenges effectively. © 2023, the Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Green computing

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Optimizing Dense Feed-Forward Neural Networks

arXiv

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arXiv 2023年

作者： Balderas, Luis Lastra, Miguel Benítez, Jose M. Department of Computer Science and Artificial Intelligence DiCITS iMUDS DaSCI E.T.S.I.I.T. University of Granada Spain Department of Software Engineering DiCITS iMUDS DaSCI E.T.S.I.I.T. University of Granada Spain

Deep learning models have been widely used during the last decade due to their outstanding learning and abstraction capacities. However, one of the main challenges any scientist has to face using deep learning models is to establish the network’s architecture. Due to this difficulty, data scientists usually build over complex models and, as a result, most of them result computationally intensive and impose a large memory footprint, generating huge costs, contributing to climate change and hindering their use in computational-limited devices. in this paper, we propose a novel feed-forward neural network constructing method based on pruning and transfer learning. its performance has been thoroughly assessed in classification and regression problems. Without any accuracy loss, our approach can compress the number of parameters by more than 70%. Even further, choosing the pruning parameter carefully, most of the refined models outperform original ones. We also evaluate the transfer learning level comparing the refined model and the original one training from scratch a neural network with the same hyper parameters as the optimized model. the results obtained show that our constructing method not only helps in the design of more efficient models but also more effective ones. © 2023, CC BY-NC-ND.

关键词： Climate change

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Optimizing Convolutional Neural Network Architecture

arXiv

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arXiv 2023年

Convolutional Neural Networks (CNN) are widely used to face challenging tasks like speech recognition, natural language processing or computer vision. As CNN architectures get larger and more complex, their computational requirements increase, incurring significant energetic costs and challenging their deployment on resource-restricted devices. in this paper, we propose Optimizing Convolutional Neural Network Architecture (OCNNA), a novel CNN optimization and construction method based on pruning and knowledge distillation designed to establish the importance of convolutional layers. the proposal has been evaluated though a thorough empirical study including the best known datasets (CiFAR-10, CiFAR-100 and imagenet) and CNN architectures (VGG-16, ResNet-50, DenseNet-40 and MobileNet), setting Accuracy Drop and Remaining Parameters Ratio as objective metrics to compare the performance of OCNNA against the other state-of-art approaches. Our method has been compared with more than 20 convolutional neural network simplification algorithms obtaining outstanding results. As a result, OCNNA is a competitive CNN constructing method which could ease the deployment of neural networks into iot or resource-limited devices. © 2023, CC BY-NC-ND.

关键词： Convolution

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Can persistent homology whiten transformer-based black-box models? A case study on BERt compression

arXiv

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arXiv 2023年

Large Language Models (LLMs) like BERt have gained significant prominence due to their remarkable performance in various natural language processing tasks. However, they come with substantial computational and memory costs. Additionally, they are essentially black-box models, challenging to explain and interpret. in this article, we propose Optimus BERt Compression and Explainability (OBCE), a methodology to bring explainability to BERt models using persistent homology, aiming to measure the importance of each neuron by studying the topological characteristics of their outputs. As a result, we can compress BERt significantly by reducing the number of parameters (58.47% of the original parameters for BERt Base, 52.3% for BERt Large). We evaluated our methodology on the standard GLUE Benchmark, comparing the results with state-of-the-art techniques and achieving outstanding results. Consequently, our methodology can "whiten" BERt models by providing explainability to its neurons and reducing the model’s size, making it more suitable for deployment on resource-constrained devices. © 2023, CC BY-NC-ND.

关键词： Natural language processing systems

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3D-MACNet: A Multiscale Asymetric Convolution Network for Feature Extraction and Classification of Hyperspectral images

3D-MACNet: A Multiscale Asymetric Convolution Network for Fe...

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international Conference on Computing, Communication, and intelligent systems (iCCCis)

作者： Murali Kanthi K. C. Ravi Kumar sammaiah seelothu sudha Pavani K Nuthanakanti Bhaskar t. Hitendra sarma Department of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Department of Computer Science & Engineering Sridevi Women's Engineering College Hyderabad Telangana India P.G.T-Computer Science Maulana Azad Nation Urdu University Hyderabad Telangana India Department of Information Technology Vasavi College of Engineering Hyderabad Telangana India

in recent years, deep learning's amazing feature extraction capabilities have led to its widespread adoption in hyperspectral image classification (Hsi) applications. However, traditional deep learning methods frequently produce inferior classification results because of the low discriminatory power of the retrieved features. Furthermore, a notable area of interest in research now involves the difficulty of achieving good classification performance in Hsis with a restricted number of samples. this letter offers a unique 3D multiscale asymmetric convolution network (3D-MACNet) for the extraction of features and Hsis classification to address this issue. By adapting the concept of symmetric convolution to the construction of an asymmetric convolution with three identical branches at distinct scales that can retrieve spatial-spectral characteristics from vertical, horizontal, and all perspectives. run on both the new data sets and the freely accessible standard data sets. the provided model has been proven to exceed the other cutting-edge approaches in regard to classification accuracy, according to empirical evidence.

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sharing information in android application using agile development and semantic ontology

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test engineering and Management 2020年第1-2期82卷 11679-11682页

作者： Mishra, sumit Kumar singh, V.K. shankhdhar, Gaurav Kant Department of Computer Science Engineering BabuBanarasi das Engineering College India Department of Computer Science Engineering BabuBanarasi das N.I.I.T. College India School of Basic Sciences BabuBanarasiDas University India

at the present time the web can provide as a perfect innovative surroundings for learning which is recommended by teachers and based on intuitively knowledge objects. Whereas a range of mechanical arrangements for the improvement of integrated e-knowledge sharing situations as of now exists, the foremost fitting arrangements require advance change of the execution of novel learning objects, unification of consistency and integration of learning environments, based on semantic administrations that are still within the early stages of advancement. the aim of the paper is to make a model using the advances of semantic learning objects, present it with the design of the instructive framework for semantic learning course design process and to display the experimental portion of the model's effect on the learning process. the research incorporates the use of Crystal Methodology from Agile software development Methodology to enhance the performance of the system. © 2020 Mattingley Publishing. All rights reserved.

关键词： Learning systems

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iot-Enabled sleep Monitoring Wearables: Advancements in tracking and Analysis

IoT-Enabled Sleep Monitoring Wearables: Advancements in Trac...

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computer, Communication and Control (iC4), international Conference on

作者： K. thinakaran V. Vijaya Baskar M. Ahsan shariff Manduva Chervith G. Raghavendra s. Velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

the importance of sleep to a person’s health and well-being cannot be overstated. sleep monitoring and analysis control has been transformed by the combination of internet of things (iot) technologies and wearable devices in recent years. this system analyzes the improvements achieved in sleep monitoring wearable enabled by iot, providing a thorough and discreet method of evaluating sleep activities. Accelerometers and heart rate monitors are at the heart of these wearables, which also have complex processing units and connectivity modules. insightful sleep quality analysis, precise sleep stage separation, and real-time data collecting are all made possible by this complementary relationship. Wearables may track sleep patterns, interruptions, and effectiveness by recording motion, heart rate variability, and brain activity through various sensors. smart cloud interfaces that provide sleep metrics and trends improve the user experience and allow a deeper analysis of a person’s sleeping habits. in addition, these wearables may easily connect to smart home systems, enhancing sleep quality by modifying the house’s lighting and audio settings depending on the individual’s sleeping habits. the data gathered by these wearables has wider uses beyond personal sleep monitoring, such as helping the system better understand and treat sleep conditions.

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Agricultural Analysis of Machine Learning Algorithms for Crop Prediction

Agricultural Analysis of Machine Learning Algorithms for Cro...

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international Conference on i-sMAC (iot in social, Mobile, Analytics and Cloud) (i-sMAC)

作者： Laksh savaliya Manav sapovadiya Dweepna Garg Premal Patel Milind shah Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) India Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Farming holds great importance in india, a country that depends significantly on agriculture. According on the 2022-23 census, the proportion of Gross Value Added (GVA) contributed by agricultural and associated sectors to the overall indian economy is 18.3%. When choosing which crop to cultivate and farm, it is important to examine several factors such as production rate, environmental conditions, soil type, temperature and pH .the project addresses the critical need for accessible information in agriculture, revolutionizing farmers decision-making with personalized crop management recommendations. Also, the proposed solution to this problem involves the use of an ensemble model with a majority voting technique. the model utilizes K-Mean, Random Forest, Linear Regression, and support Vector Machine as learners to recommend a crop based on site-specific parameters. this approach ensures high accuracy and efficiency in the crop recommendation system. the hybrid method combining K-means and random Forest achieved the best accuracy of 99.77% compared to other algorithms. Hence, the proposed approach will assist farmers in selecting the appropriate seed and identifying plants based on soil specifications, thereby enhancing crop yield. Maximize efficiency and generate financial gain from this approach. Python is used for programming, and the libraries Pandas, NumPy, scikit-Learn, Matplotlib, and seaborn are employed.

关键词： support vector machines Accuracy temperature soil Programming Agriculture seeds (agriculture) Random forests Recommender systems Python

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Factory Floor transformation for Achieving Operational Excellence with iot and Machine Learning

Factory Floor Transformation for Achieving Operational Excel...

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Advances in Modern Age technologies for Health and engineering science (AMAtHE), international Conference on

作者： t. J. Nagalakshmi P. santhuja J. Rangarajan s. Muthukumarasamy N. Mohankumar s. Velmurugan Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department oj Etectronics and Communication Engineering Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Computer Science and Engineering S.A.Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

in today's competitive industry, manufacturers seek novel approaches to improve operations and enhance efficiency. to achieve Operational Excellence, this abstract provides a Factory Floor transformation plan that uses the internet of things (iot), Machine Learning, and the Random Forest algorithm. the Factory Floor transformation employs i.t.sensors and devices throughout production. these sensors monitor machine operation, product quality, and energy usage in real-time. Using data preprocessing, feature engineering, and predictive modeling, a strong Machine learning framework is created. the Random Forest algorithm, which handles complicated, multi-dimensional data, powers Machine Learning. Random Forest is appropriate for manufacturing complex problems because its ensemble learning technique yields reliable and accurate forecasts. Predictive maintenance, quality control, and resource optimization are enabled by the model's previous training and real-time updates. Factory Floor transformation provides enterprises with a complete Operational Excellence solution. Companies may minimize production bottlenecks, downtime, defects, and resource allocation by using iot data and Machine Learning. this abstract illustrates how this method may transform production and make it more efficient and competitive.

关键词： industries Machine learning algorithms Machine learning Quality control Prediction algorithms Production facilities sensors

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Novel and Robust Energy Efficient Protocol for Authentication Purpose in iiot

Novel and Robust Energy Efficient Protocol for Authenticatio...

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Research Methodologies in Knowledge Management, Artificial intelligence and telecommunication engineering (RMKMAtE), international Conference on

作者： D Kirubakaran sasirekha s Velmurugan s saravanan Rekha R Raji Panurangan Department of Computer Science and Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chennai Tamil Nadu India Department of Electronics and Communication Engineering T J S Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu India

An developing technology called the internet of things (iot) is anticipated to offer answers for several industrial industries. Wireless sensor networks (WsNs), a foundational iot technology, may be utilized to gather the necessary environmental characteristics for certain applications. security in WsN has grown to be a huge concern as a result of the sensor node's resource limitations and the wireless channel's open nature. to ensure the legitimacy of data access in WsN, authentication may be utilized as a fundamental security function. For various security needs, Chang and Le have presented two authentication techniques for WsN. However, their protocol has significant security and functionality flaws and is unable to provide appropriate mutual authentication. to address the shortcomings of earlier methods, we introduce a three-factor user authentication scheme for WsN. the proposed protocol's security is examined, and its performance, functionality, and security are contrasted with those of other relevant protocols. the comparison and simulation findings by Ns-3 demonstrate how reliable and resource-efficient the suggested protocol is for iot applications.

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