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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

Existing research on speaker and emotional voice conversion often focuses on separate tasks, neglecting their joint exploration. Furthermore, the limited availability of emotional corpora for target speakers poses a significant challenge for training robustness and generalized models. This paper proposes an improved scheme for speaker-emotion voice conversion with limited target speaker's emotional corpus, integrating a large language model and a pre-trained emotional speech synthesis model. It introduces several enhancements to enhance the quality of converted speech in terms of speaker similarity and emotional expressiveness. First, emotionally tagged text is generated using a large language model and emotional speech is synthesized from this text using a fine-tuned pre-trained emotional speech synthesis model. Then, a speaker-emotion voice conversion model is co-trained with both synthesized and real target emotional speech. Finally, the model is fine-tuned with the real target emotional speech to further boost the speaker and emotional similarity. © 2025 IEEE.

关键词： Speech enhancement

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QoS-Aware Resource Allocation Framework for Profit Maximization and Efficient Cloud Computing

QoS-Aware Resource Allocation Framework for Profit Maximizat...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Anupam Yadav Ashish Sharma Department of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Algorithm for resource provisioning and cloud assignment for providing content in cloud based content delivery network is developed where dynamic use of large and small scale fluctuations in user demand rates are considered along with minimum lease period constraint. Virtual machines are simulated computers within a physical computer that can run their operating systems and applications. Intelligent Resource Allocation (IRA) is a QoS-aware resource allocation technique that uses advanced technologies like machine learning and artificial intelligence to optimize resource distribution in real-time. Profit-driven scheduling algorithms for cloud public service (PS) scheduling focus on optimizing resource allocation to maximize profit while prioritizing tasks related to public services. First-Come-First-Serve is the scheduling technique in which incoming tasks are served according to the first come first served procedure and the first arriving task is served first. Using entropy-based adaptive neuro-fuzzy inferences system (E-ANFIS) that combines fuzzy logic and neural networks, many factors can be analyse in real- basic for optimizing resource allocation and utilization. The conclusions established that the proposed technique, E-ANFIS, exhibits superior performance to the others, achieving an energy efficiency of 89%. Future works of this study can be direct more research efforts towards enhancing the application of the framework to multi-cloud and edge computing to integrate and optimise resources within different computing paradigms.

关键词： Fuzzy logic computers Cloud computing Adaptive systems Heuristic algorithms Quality of service Machine learning Energy efficiency Resource management Edge computing

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Impact Of Social Media Apps On Public Health: An Overview with Analysis

Impact Of Social Media Apps On Public Health: An Overview wi...

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

作者： Rawat, Vandana Amoli, Mohit Ruwari, Shalini Rajput, Suraiya Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Glocal University Department of Business and Commerce Saharanpur India

ISBN: (纸本)9798350323917

Social media has grown to be a significant element of modern life and has both beneficial and harmful effects on people's health. Several parents and activists have recently voiced their worries about the potentially negative influence of social media use. Some research indicates that using social media would use very well result in poor mental health outcomes including suicidality, loneliness, and a significant decrease in empathy. Other trials either didn't establish any risk or suggested that some people might be benefited from social media. Amongst the consequences of high usage of Social media, mental illness, panic disorder, and diminished tolerance is being increased. According to the findings of different age groups concluded that it is giving several advantages but it is harmful also so in this research, an analysis is being done of social media usage in several generations. However, unclear registration revealed lifelong suicidal ideation, demonstrating that this particular posture may be a sign of more serious issues. This research focuses on how social media affects people's life. © 2023 IEEE.

关键词： Diseases

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A Multimodal Deep Learning Approach for Advancing Liver Disease Diagnosis and Prognosis Prediction 2

A Multimodal Deep Learning Approach for Advancing Liver Dise...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Kumar, Mukesh Shelke, Nitin Arvind Singh, Jagendra Sharma, Kamal Nain Sharma, Rajat Kumar, Rakesh The Technological Institute of Textile & Sciences Electronics and Communication Engineering Bhiwani India Bennett University School of Computer Science Engineering & Technology Greater Noida India Lovely Professional University School of Computer Application Jalandhar India G D Goenka University Department of Computer Science and Engineering - SOES Gurugram India Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798350368109

Using a multimodal deep learning framework, we have demonstrated the possibility to predict liver disease subtyping and prognosis with high accuracy. The framework includes over 1600 patients, for which medical imaging and clinical data have been collected. Convolutional Neural Networks (CNNs), Recurrent Neural Network (RNNs), and Long short-term memory (LSTMs) have been combined in the work to extract features from the input data and demonstrate correlation between input and output and model complexity. Such an approach allowed analyzing images and predicting liver diseases based on structural abnormality analysis with the use of CNNs. RNNs and LSTMs, in their turn, allowed combining features for multimodal data source analysis and sequential clinical data analysis providing information about patient history. The obtained results were accurate for each feature extraction and modeling, with the best performance demonstrated by CNN with RNN where the accuracy was 97.8% and another CNN with LSTM where the accuracy was around 94.5%. The model proved to be feasible and accurate in the context of its application to hepatology since multiple data sources were combined in the work. This approach allows accounting for all modes of data source and offers a comprehensive assessment including disease diagnosis and prognosis. The obtained results can be also applied to clinical practice where doctors can add data about their patients to receive more information on diagnosis or prognosis for better decision-making. Finally, the idea to use a multilayered feature from multimodal data sources can be transferable to other types of sequential and structural data and applied to other diseases for their diagnosis and prognosis. However, it is essential to test the framework in other settings to refine it or find limitations to its application. © 2024 IEEE.

关键词： Convolutional neural networks

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Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Bioinspired Clustering in UWSNs Using Monarch Butterfly Opti...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Indira, G. Rani, Ruchika Chiranjevi, D. Husain, Saif O. Yogi, Rahul Nandhini, T.J. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Aditya Engineering College Department of Computer Science & Engineering India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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Extrapolative Preservation Management of Medical Equipment through IoT 1

Extrapolative Preservation Management of Medical Equipment t...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tripathi, Rajeev Mishra, Vinay Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gupta, Ashulekha School of Management Sciences Department of Computer Application and Sciences Lucknow India School of Computer Application Babu Banarasi das University Lucknow India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations. © 2023 IEEE.

关键词： Failure Management Internet of things IoT Medical Equipment Maintenance Predictive Maintenance Sensors

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Predictive modeling for concrete properties under variable curing conditions using advanced machine learning approaches

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Asian Journal of Civil engineering 2024年第8期25卷 6249-6265页

作者： Mungle, Nischal P. Mate, Dnyaneshwar M. Mankar, Sham H. Tale, Vithoba T. Mehta, Ankita Dhale, Shrikrishna A. Vairagade, Vikrant S. Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Mechanical Engineering JSPM’s Rajarshi Shahu College of Engineering Maharashtra Pune 440033 India Department of Mechanical Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune 412101 India Department of Computer Science and Application Ramdeobaba University Maharashtra Nagpur 440013 India Department of Civil Engineering Priyadarshini College of Engineering Maharashtra Nagpur 440019 India

Such is the need for this work, since accurate concrete strength prediction at different curing conditions is critical to having structures that are both strong and long-lasting. Traditional methods for the prediction of concrete strength often lack the complexity that occurs in the interaction of the different environmental factors involved, hence leading to suboptimal practices in curing with potential structural weaknesses. Current research into this area has typically focused on disparate data sources and rather naïve modeling methods, further limiting predictive accuracy and creating a general lack of comprehensive knowledge of curing dynamics. Such limitations bring out the need for a more integrated, sophisticated predictive modeling approach to explain variability in concrete strength levels. This paper proposes a novel predictive modeling framework that will be powered by advanced machine learning techniques to take up these challenges. It will adopt a multimodal data integration approach driven by a combination of sensor data related to temperature, humidity, and strain gauges;environmental data related to weather conditions and atmospheric pressure;and historical records, such as mix design and curing duration, further leveraging techniques from data fusion, including the Kalman filter and Bayesian networks. This will be further integrated into a unified, enriched dataset, encapsulating the complex interaction of factors influencing concrete strength. In the present work, this is a chosen approach: hybrid modeling with ensemble learning using XGBoost for the prediction of static features, and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies. In this case, a combination of these models via weighted averaging or stacking improves the accuracy of the predictions to a very great extent: the R² increased from 0.85 to 0.92, and MAE levels by 10–15%. In addition to that, AutoML with Feature Tools implements advanced feature engineering

关键词： Concrete strength Data fusion Ensemble learning Machine learning Predictive modeling

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Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework 4

Oddity Based Botnet Recognition Utilizing AI Model in the In...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Rani, Rupa Chakraborty, Sudeshna Singh, Pooja Varshney, Neha Abes Institute of Technology Computer Science & Engineering U.P Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India School of Computer Application and Technology Galgotias University UP India Abes Institute of Technology Department of Computer Science & Engineering U.P Ghaziabad India

ISBN: (纸本)9798350393248

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with © 2023 IEEE.

关键词： Cybersecurity

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Integrating Smart Farming with AI Techniques for Crop Disease Prediction 15

Integrating Smart Farming with AI Techniques for Crop Diseas...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singla, Monika Gupta, Deepali Goyal, Deepam Dang, Rajeev Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh group of colleges Chandigarh School of Business Department of Computer Application Jhanjeri140307 India UIET PUSSGRC Department of Mechanical Engineering Punjab Hoshiarpur India

ISBN: (纸本)9798350370249

Farming encompasses the act of cultivating land, growing crops, and raising animals, which is crucial for the growth of country's economy. For every country, agriculture contributes to approximately 70% of the main source of income. Historically, farmers have used traditional agricultural methods, which though time-consuming, often yielded imprecise results, leading to decreased output. Precision agriculture stands out as a viable solution to this issue, optimizing production by accurately identifying the necessary measures at an appropriate time. Elements of precision farming include meteorological prediction, soil assessment, crop recommendation for cultivation, and determination of optimal quantity of fertilizers and pesticides. Precise farming utilizes sophisticated techniques such as Machine learning, IoT, Data analytics and Data Mining to gather data, train systems, and forecast outcomes. Precision farming utilizes technology to minimize manual effort and enhance output. Recent agriculture challenges, such as crop failures because of low rainfall and soil sterility, raise the need for further exploration. This article aims to explore efficient methods used for crop management and harvesting in response to environmental changes. Several machine learning and deep learning models have come out with encouraging results in crop disease detection, thereby enhancing crop quality and productivity. © 2024 IEEE.

关键词： Smart agriculture

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