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The Importance of Including Sustainable Development Goals (SDGs) in the Educational Process of Engineers and Electrical Engineers 65

The Importance of Including Sustainable Development Goals (S...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Patlins, Antons Institute of Industrial Electronics Electrical Engineering and Energy Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798350365771

The integration of Sustainable Development Goals (SDGs) into the educational process of engineers and electrical engineers is pivotal for fostering a sustainable future. This paper discusses the significance of embedding SDGs in engineering education and presents methodologies for incorporating these goals into various study subjects. The study explores current trends, proposes innovative pedagogical approaches, and provides practical recommendations for educators and institutions. The outcomes suggest that incorporating SDGs not only enhances the relevance of engineering education but also equips future engineers with the necessary skills and mindset to address global sustainability challenges. © 2024 IEEE.

关键词： Sustainable development goals

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Improving group recommendation using deep collaborative filtering approach

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International Journal of information technology (Singapore) 2023年第3期15卷 1489-1497页

作者： Yannam, V. Ramanjaneyulu Kumar, Jitendra Babu, Korra Sathya Sahoo, Bibhudatta Computer Science and Engineering National Institute of Technology Rourkela Sector 1 Odisha Rourkela 769008 India Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Jagannathagattu Andhra Pradesh Kurnool 518007 India

The recommender system’s objective is to recommend items to users. Providing personalized suggestions to users is a challenging task. The traditional recommendation system recommends items for individual users. However, in recent times the popularity of group activities has increased, like watching movies with family, holiday trips with friends, etc. While predicting the rating information, existing methods disregard the metadata information. Therefore, the proposed approach used metadata information for prediction. It addresses data sparsity. This paper presents a rating prediction for group that leverages multilayer perceptron and General Matrix Factorization using metadata with Neural Collaborative Filtering techniques. The proposed approach is discussed in two steps. The first step is to learn from group-item interaction, perform one hot encoding for group and item, and then utilize this information to perform dot product by applying the GMF layer. In the second step, learn group-item interactions using group metadata and item metadata. This information is concatenated using the MLP layer. Finally, the GMF and MLP layer combined to get the final prediction ratings. The proposed approach has the advantage uses metadata to alleviate the cold start issue in group recommendation scenarios. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Collaborative filtering General matrix factorization Group recommender system Multi-layer perceptron Neural collaborative filtering

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Potato Leaf Diseases Detection Using Machine Learning And Deep Learning

Potato Leaf Diseases Detection Using Machine Learning And De...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shiffa, T.S. Suchithra, M.S. Vijayakumar, Aiswarya Department of Computer Science Kerala Irinjalakuda India Indian Institute of Information Technology Department of Computer Science and Engineering Kerala Kottayam India

ISBN: (纸本)9798350394580

Agriculture plays a vital role in sustaining human life, providing the food and resources needed for survival. With the advent of digitalization, technology has permeated every aspect of our lives, including agriculture. The potato, a versatile tuber, is a widely enjoyed and commonly consumed staple in diets across the globe. To ensure a prosperous potato production, establishing a robust food security system becomes imperative, given its high nutritional value in terms of vitamins and minerals. Nonetheless, it is crucial to address diseases affecting potatoes including early blight and late blight. The manual assessment of these leaf diseases is labor-intensive and inconvenient. In the present investigation, we have implemented a system that utilizes a combination of machine learning and deep learning methodologies to classify two distinct types of diseases in potato plants, relying on the assessment of their leaf conditions. We utilized a range of models including support vector machine, naive bayes, K-Nearest Neighbor, Decision Tree, Random Forest, convolutional neural network, Sequential2, and VGG 16 to establish a highly accurate classification system. Notably, our experiment yielded an impressive accuracy of 95.36% within the initial 10 epochs of VGG 16 training, affirming the viability of the deep neural network approach. This research has made a substantial contribution to the agricultural sector and has provided valuable insights for farmers to effectively classify Potato leaf Disease, yielding optimal results. © 2023 IEEE.

关键词： convolutional neural network Decision Tree deep learning K-Nearest Neighbor Leaf disease classification machine learning naive bayes potato plant Random Forest Sequential2 support vector machine VGG16

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Investigating and Implementing the Security of Mobile Devices in Wireless Networks 3

Investigating and Implementing the Security of Mobile Device...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Saket Yadav, Dhananjay Sandeep Rao, P. Nishanthi, R. Gawali, Piyush P. Dhivrani, Rekha Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Cell devices have become increasingly popular in today's digital global. They may be becoming extensively used in many unique networks, including wireless ones. As such, it's vital to ensure that those gadgets are competently secured to protect them from potential protection breaches or malicious attacks. This paper examines the technologies to be had for investigating and enforcing the security of mobile devices in Wi-Fi networks, the contemporary protection answers available, and how those may be used to ensure that cell tool integrity is maintained. This paper also discusses how those solutions can be used to come across and respond to malicious safety threats. ultimately, it attempts to pick out capacity dangers associated with cellular devices and capability techniques to counter these threats. © 2023 IEEE.

关键词： adequately associated increasingly maintained technologies

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Two-view attention-guided convolutional neural network for mammographic image classification

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CAAI Transactions on Intelligence technology 2023年第2期8卷 453-467页

作者： Lilei Sun Jie Wen Junqian Wang Yong Zhao Bob Zhang Jian Wu Yong Xu College of Computer Science and Technology Guizhou UniversityGuiyangChina Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Harbin Institute of Technology ShenzhenChina School of Electronic and Computer Engineering Shenzhen Graduate School of Peking UniversityShenzhenChina Department of Computer and Information Science University of MacaoTaipaChina Science for Life Laboratory KTH Royal Institute of TechnologyStockholmSweden

Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature ***,general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into *** exploit the essential discriminant information of mammographic images,we propose a novel classification method based on a convolutional neural ***,the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal(CC)mammographic *** features extracted from the two-view mammographic images contain complementary information that enables breast cancer to be more easily ***,the attention block is introduced to capture the channel-wise information by adjusting the weight of each feature map,which is beneficial to emphasising the important features of mammographic ***,we add a penalty term based on the fuzzy cluster algorithm to the cross-entropy function,which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the *** experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state-ofthe-art classification methods.

关键词： convolutional neural network deep learning medical image processing mammographic image

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) Security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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Causal Policy Gradient for End-to-End Communication Systems 16

Causal Policy Gradient for End-to-End Communication Systems

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

作者： Shirodkar, Shounak Banerjee, Serene International Institute of Information Technology Computer Science and Engineering Bangalore India Ericsson Research Machine Reasoning and Hybrid Ai Bangalore India

ISBN: (纸本)9798350383119

End-to-End (E2E) learning of communication systems can be realized with autoencoders when the channel model is differentiable. However, due to various time-varying unknown factors such as signal attenuation, hardware distortions, and multi-path fading, the channel model in practice is often non-differentiable. For such cases, the state-of-the-art demonstrates that it is possible to learn E2E reconstruction of the transmitted data, using Gaussian policy gradient or Deep Deterministic Policy Gradient (DDPG) algorithms. However, a few shortcomings of the proposed approaches include: (a) the solution not scaling well with the number of channel uses, (b) needing long coherence time of the channel compared to the rate at which examples can be processed at training, (c) poor sample efficiency during training and slow convergence especially for larger constellations, and (d) poor generalization. Uncovering the causal relationships between: the state (i.e. transmitted symbols), the action (i.e. constellation shaping), and the reward (i.e. the loss between the received and the transmitted symbols), has potential to address the above limitations. We propose two algorithms for causality-inspired end-to-end learning, where the causal relationship between: (a) the state and the action, and (b) the action and the reward shows up to 90% faster convergence, compared to the state-of-the-art variants of policy gradient algorithms. For the trained model, the Block Error Rate (BLER) at 17dB Signal to Noise Ratio (SNR) is less than 0.005, for various constellation shapes. To the best of our knowledge, this is the first demonstration of causality-inspired end-to-end learning of communication systems, without a prior channel model. © 2024 IEEE.

关键词： causal reinforcement learning domain generalization End-to-end learning of communication systems non-differentiable channel model sample-efficient learning

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Analysis of Clinical Decision Support System in Healthcare Industry Using Machine Learning Approach 8th

Analysis of Clinical Decision Support System in Healthcare I...

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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Takale, Dattatray G. Mahalle, Parikshit N. Sakhare, Sachin R. Gawali, Piyush P. Deshmukh, Gopal Khan, Vajid Banchhor, Chitrakant B. Maral, Vikas B. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of AI & DS Vishwakarma Institute of Information Technology SPPU Pune Pune India Department of Computer Engineering KJ College of Engineering and Management Research SPPU Pune Pune India

ISBN: (纸本)9789819956517

This research presents a novel machine learning-based clinical DSS for intensive care unit (ICUs). The system aims to predict mean arterial pressure (MAP) values in real-time and provide decision support for healthcare professionals. The initial step in the system's two-stage methodology is applying HTM to streams of vital signs in order to make real-time, unsupervised predictions for each vital sign. The predicted vital sign streams are then processed using feature extraction and selection techniques, and a prediction model is used to predict the MAP value. The system’s accuracy and duration from decision to event were measured using a number of different machine learning techniques, including long short-term memory (LSTM), support vector machine (SVM), and decision tree classifiers. Critical care nurses and doctors may benefit from the suggested LSTM + HTM model since it has a good accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) for predicting MAP levels. By allowing for the early identification of changes in MAP values and providing prompt decision assistance for healthcare personnel, the suggested system is a viable tool for improving patient outcomes in critical care units. The system’s performance must be tested on a bigger dataset, and the possibility of incorporating it into current clinical processes must be investigated in more depth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Long short-term memory

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Indoor Navigation for Mobile Devices using Augmented Reality with Wi-Fi RTT 11th

Indoor Navigation for Mobile Devices using Augmented Reality...

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

作者： Christy Jeba Malar, A. Deva Priya, M. Karthick, S. Sudharsan, S. Suhita, S. Vidhya, K. Department of Information Technology Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi–NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819717231

Indoor Positioning (IP) technology aids in providing location-based services within a campus. Indoor navigation applications are deployed in universities, airports, hospitals, shopping malls and railway stations to guide visitors. 2D navigation map is not sufficient to guide users inside campus because it is difficult to implement in a multi-floor environment. Similarly, Global Positioning System (GPS) for indoors particularly in multi-storeyed structures is inadequate for producing consistent positional data. As Wi-Fi-based positioning involves low hardware installation cost and high availability, it is widely used within buildings. In this paper, a simple and cost-effective Augmented Reality (AR)-based navigation application is designed to guide users within a campus with multiple floors. The proposed system uses Wi-Fi Round Trip Time (RTT) technology available in smart phone along with AR technology to locate users and provide immersive navigation experience to users. Investigations are performed within college campuses with multi-storey buildings. The accuracy is about 4–6 m along the pathway to the destination. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Augmented reality

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An Innovation Analysis of Semantic Text Classification Using Deep Belief Networks

An Innovation Analysis of Semantic Text Classification Using...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Mishra, Shashank Jebaraj, N.R. Solomon Saraswat, Pankaj College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

The study of Semantic textual content class (STC) is regularly a critical component of natural Language Processing (NLP). It looks at what is particularly critical in regions, including sentiment evaluation, information retrieval, and other text understanding obligations. Conventional machine learning models and Support Vector Machines (SVMs) and Naïve Bayes have been used for these tasks. However, recent research has explored Deep notion Networks (DBNs) in this regard. This paper affords an innovation evaluation of DBNs for STC issues, which mainly seeks to research the overall performance of the Deep notion Networks in evaluating other present techniques. To evaluate the overall performance of DBNs in this venture, we employ a Python-based, totally data-rich benchmark dataset used for sentiment evaluation. Outcomes demonstrate that the accuracy of DBNs may be similar to or higher than SVMs and Naïve Bayes, reaching almost identical overall performance. The results advocate that the DBN technique for STC is a feasible and competitive method and offers a much better and correct algorithm for NLP duties. © 2024 IEEE.

关键词： competitive conventional demonstrate Python-based Semantic textual

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