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Communication & Computing (WCONF), World Conference on

作者： Yogesh Kumar Sharma Senthil Athithan Savya Sachi Ajay Kumar Singh Arpit Jain Suman Devi Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Guntur AP Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of information technology Lalit Narayan Mishra College of Business Management Muzaffarpur Bihar India Allenhouse Institute of Technology Kanpur India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Guntur A.P. India Department of Computer Science and Engineering Chaudhary Devi Lal University Sirsa Haryana India

Copy-Move forgery detection (CMFD) is one of the most active study topics in blind image forensics. Most known algorithms are based on block and key-point approaches or a mix. Recently, various deep convolutional neural network approaches have been utilised in image classification, image forensics, image hashing retrieval, and other areas, and they have outperformed the previous way. The paper proposes a unique copy-move forgery detection system based on a convolutional neural network. The suggested technique takes a trained model from an extensive database such as ImageNet and minimally modifies the net structure using tiny training examples. The experimental findings indicate that the approach we presented outperforms the counterfeit picture created automatically by the computer using a simple image copy-move operation.

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DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

DNN Partitioning and Assignment for Distributed Inference in...

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International Conference on Distributed Computing Systems

作者： Yuepeng Li Deze Zeng Lin Gut Song Guo Albert Y. Zomaya School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Distributed Deep Neural Network (DNN) inference is a promising technology to explore the distributed resources in edge cloud to realize edge intelligence. Meanwhile the inherent resource sharing nature of edge cloud infrastructure also raises serious concerns on security and privacy. Software Guard Ex-tensions (SGX) emerges as a potential hardware-level solution but its limited secure memory (i.e., enclave page cache) imposes new challenges, especially in contrast to memory-hungry DNN models. A task's performance will be severely affected when its memory footprint is beyond the enclave page cache size, due to expensive secure page swapping. In this case, how to appropriately partition a DNN model and assign the partitions to distributed edge servers to efficiently utilize edge resources for fast secure inference becomes a challenging problem. In this paper, we first show that this problem is NP-hard. We further propose a MEmory -aware Distributed Inference Acceleration (MEDIA) algorithm, whose guaranteed approximation ratio is also formally analyzed. We have implemented a prototype system and applied some well-known representative DNN models to evaluate MEDIA's performance. Through extensive experiments, we verify the efficiency of MEDIA by the fact that it reduces the inference time by 19.5%-38.1 % in comparison with state-of-the-art approaches.

关键词： Computational modeling Prototypes Artificial neural networks Media Approximation algorithms Inference algorithms Software

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Deep Learning Advancements in E-commerce Supply Chain management in Forecasting and Optimization Strategies

Deep Learning Advancements in E-commerce Supply Chain Manage...

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Sustainable Energy and Future Electric Transportation (SEFET), International Conference on

作者： Jagendra Singh Nitin Arvind Shelke Kamal Upreti Fariyah Banu Jamaluddin Saiyad Prakash Divakaran Khadilkar Sujay Madhukar School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science CHRIST (Deemed to be University) Ghaziabad India Department of Commerce Bath spa university Rak Ras Al-Khaimah United Arab Emirates Department of management Himalayan University Itanagar India Education and Research KIT's Institute of Management Kolhapur India

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

In this study, the influence of deep learning technologies on the optimization of supply chain management in the context of the e-commerce industry is examined. Using a dataset of historical data of sales, inventories, market fluctuations, and customer and supplier details, I investigate the efficiency of different deep learning models to predict demand and facilitate the optimal balance of inventories. Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and a model proposed by the authors are defined and applied, considering their accuracy, precision, recall, and F-1 score. The results show that the proposed model outperforms traditional products, achieving 97.5% of accuracy. In the context of the comparative analysis, the specific features of CNN, LSTM, and RNN are revealed, helping to understand the benefits and drawbacks of each recommendation. As a result, the proposed model proves that deep learning technologies have the power to change the approach to predictive analytics and supply chain management, allowing practitioners to focus on strengths and overcome the weaknesses of their structures. The impact of data preprocessing and hyperparameters is also considered along with the necessity to choose the most appropriate model evaluation technique. In the future, it is possible to implement other complex deep learning models, integrate additional data, and address the problem of data scaling and heterogeneity. In the era of modern technologies, e-commerce organizations should take these findings into consideration to discover the potential of deep learning, improve supply chain performance, reduce costs, and attract clients. This research contributes to the topic of using deep learning technologies in supply chain management, promoting innovation, and changes that may affect the industry drastically.

关键词： Deep learning Industries Analytical models Supply chain management Recurrent neural networks Supply chains Predictive models Electronic commerce Long short term memory Optimization

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Hadoop Map Reduce Techniques: Simplified Data Processing on Large Clusters with Data Mining 6

Hadoop Map Reduce Techniques: Simplified Data Processing on ...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Suresh, S. Rajesh Kumar, T. Nagalakshmi, M. Bennilo Fernandes, J. Kavitha, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha Nagar Thandalam Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management Department of Computer Science and Engineering Telangana Dundigal India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar India Hindusthan Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Coimbatore India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Data mining applications have become outdated and outmoded in recent years. The use of incremental processing to refresh mining results is a promising method. It makes use of previously saved states to save time and energy on re-computation. In this research, we offer a novel increment processing addition to the Map Reduce, the most extensively used methodology for mining the big data by using the Naive Bayes, the J48, and the Random Forest algorithms. Map reduction is a programming model for simultaneous processing and generation of massive amounts of data. We examine Map Reduce employing Naive Bayes, J48, and Random Forest algorithms with a variety of processing features for efficient mining that also saves energy. The Naive Bayes algorithm generates more energy and fewer maps. Priority-based scheduling is a task that allocates schedules based on the jobs' requirements and utilization. As a result of decreasing the maps, the system's workload is reduced, and energy efficiency is improved. The experimental comparison of the several algorithm techniques (Naive Bayes, J48, and Random Forest) have applied in this article and found that the Random forest is performed better than remaining two algorithms i.e. 92%. © 2022 IEEE.

关键词： Data mining

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Sentimental Analysis through Speech and text for IMDB Dataset 4

Sentimental Analysis through Speech and text for IMDB Datase...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Sikhi, Yalavarthi Devi, S. Anjali Jasti, Sreekar Kumar Ram, M. Sitha Naveen Jindal School of Management The University of Texas at Dallas Dallas United States Department Of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas Dallas United States Department of CSE Lakireddy Bali Reddy College of Engineering Mylavaram AP Vijayawada India

ISBN: (纸本)9781665401180

In the advanced innovative present reality, most of the public are depending on different surveys for different utilizations and different products. Thus, to examine these audits and comprehend whether they are supporting or refuting about, opinion investigation can be utilized. The days for the remark surveys are currently disappearing and everybody is keen on hearing the audits so as they need not stop their functions. Thus, a slant investigation that does both through brief snippet and text can be used to complete work in sound. Along these lines, in this exploration paper, different Machine Learning and Deep Learning Models like Naive Bayes, S upport Vector Machine (S VM), Random Forest, and Multi-Layer Perceptron are used. Additionally, as of to change over voice to a message, a google API and a deep learning technique are used and best of two is exhibited and afterward sentiment analysis on those texts is performed and finally the sentiment (i.e., positive, or negative) of the speech is obtained. All these processes are carried out in real-time. © 2022 IEEE

关键词： Decision trees

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A Preliminary Study on Learners' Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach 13

A Preliminary Study on Learners' Personal Traits for Modelli...

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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Rahman, Mohammad Mustaneer Al Salem, Hassan A. Yeom, Soonja Ollington, Nadia Ollington, Robert Rahman, Md. Mujibur University of Tasmania School of ICT HobartTAS Australia Jazan University Department of Management Information Systems Al Maarefah Rd Jazan Saudi Arabia University of Tasmania Faculty of Education HobartTAS Australia University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350347319

Intelligent tutoring systems (ITS) are computer-based learning systems that use intelligent technologies to provide adaptive learning paths and contents to learners by considering their characteristics and needs. The learner model, which is a representation of different learner's characteristics, plays a key role in this adaptation. Research on educational psychology showed that emotion and learning have a hidden mutual relationship. If a learning process involves both cognitive and affective dimensions, then learning is like to be more usable, socially appropriate, acceptable and memorable, ultimately leading to significant improvement in the learner's performance and the learning experience. However, traditional ITS ignore emotional aspects in learner modelling mostly;instead, they focus on learners' cognition and motivation to maximise the knowledge to be achieved. The characteristics related to cognition are not well defined and primarily sparse when modelling a learner profile tailored to the learning domains. This study attempts to investigate the most common and relevant personal traits involved in the learning process based on the domains of learning. It also explores the most adopted techniques to identify such traits from log files of learners' interactions with ITS without using physical and physiological sensors (i.e., camera, EEG) to model characteristics learner profiles for ITS aiming to improve its adaptiveness and leverage personalised learning. © 2023 IEEE.

关键词： computer aided instruction

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Machine Learning-Based Approach for Fake News Detection

Journal of ICT Standardization

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Journal of ICT Standardization 2022年第4期10卷 509-530页

作者： Gururaj, H.L. Lakshmi, H. Soundarya, B.C. Flammini, Francesco Janhavi, V. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy Of Higher Education Manipal India Department of Information Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore India IDSIA USI-SUPSI Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Switzerland Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

In the modern era where the internet is found everywhere and there is rapid adoption of social media which has led to the spread of information that was never seen within human history before. This is due to the usage of social media platforms where consumers are creating and sharing more information where most of them are misleading with no relevance with reality. Classifying the text article automatically as misinformation is a bit challenging task. This development addresses how automated classification of text articles can be done. We use a machine learning approach for the classification of news articles. Our study involves exploring different textual properties that may be often used to distinguish fake contents from real ones. By using those properties, can train the model with different machine learning algorithms and evaluate their performances. The classifier with the best performance is used to build the classification model which predicts the reliability of the news articles present in the dataset. © 2022 River Publishers.

关键词： Classification (of information)

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The Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan 4

The Role of IOT in Creating SC'S through Ultra Fast Updation...

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4th IEEE International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2024

作者： Hariharasudhan, S. Subasree, S. Bhuvaneswari, M. Victor, Mekvin Kumar, Rintu Kumar, K. Sambath Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology India Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Biomedical Engineering TN Coimbatore India Christ University School of Business and Management Bangalore India Ies University Department of Mech Engg Ies Institute of Technology and Management M.P. Bhopal462044 India Chennai Tamil Nadu Avadi India

ISBN: (纸本)9798350360165

The idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of The circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. This paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research. © 2024 IEEE.

关键词： AI-powered systems blockchain data management healthcare IoT machine learning smart cities smart technology urban efficiency wireless sensor networks

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The Original Method of Controlling a computer Using Distance Sensors 16

The Original Method of Controlling a Computer Using Distance...

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16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer Engineering, TCSET 2022

作者： Savaryn, Pavlo Strekha, Vadym Brych, Mykola Brych, Lesia Kabak, Vitalii Polishchuk, Mykola Lutsk National Technical University Department of Digital Education Technologies Lutsk Ukraine Lutsk National Technical University Department of Computer Science Lutsk Ukraine Internet of Things and Software Defined Networks Lab Lviv Polytechnic National University Lviv Ukraine Centre for International Education Lviv Polytechnic National University Lviv Ukraine Lutsk National Technical University Department of Computer Engineering and Cybersecurity Lutsk Ukraine

ISBN: (纸本)9781665468619

The article provides a brief overview of human-computer interaction methods and systems, and the research made in this area. We have designed the architecture of the system and tested a method of controlling a computer using ultrasonic distance sensors. In the course of the experiment, the following problems have been solved: obtaining and processing data from several distance sensors, data transfer to gesture recognition algorithm, uncontrolled oscillations of sensor indicators. The suggested method of controlling a computer can be used in ubiquitous computing. © 2022 IEEE.

关键词： Human computer interaction

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Enhanced Recurrent Neural Network for Reducing Carbon Foot Printing in Industry 6

Enhanced Recurrent Neural Network for Reducing Carbon Foot P...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Chande, Ketan Kanekar, Rahul Nair, Kiran Amandykova, Dina Addanke, Supriya Zhaina, Tolegen Hospitality NarseeMonjee Institute of Management Studies Navi Mumbai-NMIMS University Maharashtra India Abu Dhabi School of Management Department of Management United Arab Emirates International Education Corporation Department of Architecture Kazakhstan 050042 India Sri Padmavati MahilaVisvavidyalayam School of Engineering & Technology Department of Computer Science & Engineering Tirupati517502 India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

At present, green communication technology is receiving a significant research attention. The increasing research interest on green communication can also undermine the environment. Measuring the green communication intensity of various products, companies and processes is being carried out globally by following the rule that only the related effects are manageable, which is expressed as a carbon footprint. Green detections are having a direct, large-scale impact on carbon productions. The green initiatives can effectively reduce carbon productions by improving the energy efficiency. In summary, green discovery directly affect carbon production. This research work has attempted to reduce the carbon footprint energy by using Enhanced Recurrent Neural Network (ERNN). From an investment perspective, carbon footprint analysis can assist in evaluating a company's overall and comparative performance. It can be used as a tool to manage and evaluate the performance of a company. Effective production management demonstrates the quality of operations and can provide a significant competitive advantage. © 2022 IEEE.

关键词： Carbon footprint

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