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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Amit Kumar Nidhi Soni Sulaymonov Dilshod Abduraximovich Hassan M. Al-Jawahry S. Jayasree Gnanajeyaraman Rajaram Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Urology and Oncology Fergana Medical Institute of Public Health Fergana Uzbekistan Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Time management is an important aspect of our daily lives and this research aims at creating an intelligent assistant that will be able to support an individual in managing their time appropriately using application of the Bayesian Neural Networks (BNNs). Many of the conventional time management strategies are posed to be ineffective since they cannot be changed depending on the unique person and situations in a similar way personal organization systems cannot be changed to suit the unique person and situations. Therefore, the use of Bayesian neural networks (BNNs) to perform inference makes them ideal for this purpose given that BNNs use probability and Bayesian inference to work under uncertainty. The involved process involved utilization of effective data gathering methodologies to obtain comprehensive information on users’ activities, preferences, and productivity to develop the BNN model. The plan is that the latter was integrated into the context of a mobile application where, similar to an intelligent agent of an organism, it adapts in real time and suggests the most efficient ways of planning the time. A number of user tests pointed at level 3: Most of the users reported high levels of satisfaction and ranked productivity gains throughout the different activities under study. Escalating accuracy was also affirmed by the result analyses owing to the continual refining of the model in subsequent phases. This work demonstrates the possibility of the proposed BNN approach to change time management approaches and present a more effective approach of time management in line with the need of the user. As such, future work will comprise a continuation of the model’s development with the addition of new functions that will complement the existing features and make the tool even more useful and appealing to potential users.

关键词： Productivity Surveys Uncertainty Accuracy Neural networks Signal processing Bayes methods mobile applications Telecommunication computing Time factors

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Challenges in metaverse in problem-based learning as a game-changing virtual-physical environment for personalized content development

Challenges in metaverse in problem-based learning as a game-...

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6th Smart Cities Symposium (SCS 2022)

作者： P.K. Dutta M. Bose A. Sinha R. Bhardwaj S. Ray S. Roy K.B. Prakash School of Engineering and Technology Amity University Kolkata Kolkata India School of Computer Application Amity University Kolkata Kolkata India Department of Mathematics Amity University Kolkata Kolkata India Senior Researcher Petersburg Polytechnic University Artificial Intelligence & Data Science Jio Institute Navi Mumbai India Department of Computer Science and Engineering Konneru Lakshmaih Education Foundation

The benefits and drawbacks of using the metaverse in the field of education are discussed, along with the possibilities and potential issues it will open up. Utilizing the idea of personalised knowledge transfer and the use of an immersive environment to match students' learning pace using horizon classroom, the Metaverse environment can add a new dimension to the field of educational technologies. We have also highlighted the virtualization-based redesign of educational environments using a personalised cloud, which is anticipated to improve learning for students by making it more affordable, collaborative, and useful. The goal is to build a metaversity that can serve as a communication tool for teachers and students anywhere in the world by providing each with an individual avatar. This platform will offer a virtual setting for students to socialise with their friends and peers and receive peer-to-peer learning, which is lacking in the current e-learning environment. The students won't need an entrance score card; instead, they will need critical learning skills to advance and mature.

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Blockchain-Enabled Smart Contracts: An Evolutionary Algorithm Approach for Automated Financial Agreement

Blockchain-Enabled Smart Contracts: An Evolutionary Algorith...

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International Conference on Computing and Networking technology (ICCNT)

作者： J Chandra Sekhar Arun B Prasad Shantanu Kanade Anuradha Kanade C Dhilipan B Kiran Bala NRI Institute of Technology Guntur India Institute of Law Nirma University Ahmedabad India Symbiosis School for online and Digital Learning Symbiosis International University Pune Maharashtra India School of Computer Science and Application MIT World Peace University Pune Maharashtra India CMS Business School Jain Deemed to be University Bangalore India Department of AI & DS College of Engineering Trichy India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Blockchain technology has revolutionized various industries by introducing decentralized and secure systems. Smart contracts, self-executing contracts with the terms of the agreement directly written into code, represent a significant advancement in automating and enhancing the efficiency of financial agreements. This paper proposes an evolutionary algorithm approach to further optimize the implementation of smart contracts on the blockchain for automated financial agreements. The evolutionary algorithm, inspired by the principles of natural selection and genetic algorithms, aims to enhance the adaptability and performance of smart contracts over time. Through a continuous process of selection, crossover, and mutation, the algorithm evolves the codebase of smart contracts to better suit the dynamic and evolving nature of financial markets. This approach not only improves the efficiency of automated financial agreements but also enables smart contracts to adapt to changing market conditions, regulatory requirements, and user preferences. Furthermore, the security implications and challenges associated with the evolutionary algorithm are examined, along with potential strategies for mitigating risks. In conclusion, the integration of evolutionary algorithms with blockchain-enabled smart contracts represents a promising avenue for advancing the automation of financial agreements. This approach not only addresses the challenges of adaptability and optimization but also contributes to the ongoing evolution of blockchain technology in the realm of decentralized finance. The findings of this research have implications for financial institutions, regulators, and developers seeking to leverage the full potential of smart contracts in an ever-changing financial landscape.

关键词： Industries Regulators Heuristic algorithms Smart contracts Finance Evolutionary computation Blockchains Security Optimization Genetic algorithms

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SeaConvNeXt:A Lightweight Two-Branch Network Architecture for Efficient Prediction of Specific IHC Proteins and Antigens on Hematoxylin and Eosin(H&E)Images

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Big Data Mining and Analytics 2024年第4期7卷 1212-1236页

作者： Yuli Chen Guoping Chen Guoying Shi Yao Zhou Jiayang Bai Germán Corredor Cheng Lu Xiujuan Lei School of Computer Science Shaanxi Normal UniversityXi’an 710119China Department of Pathology Shaanxi Provincial Hospital of Traditional Chinese MedicineXi’an 710003China Wallace H.Coulter Department of Biomedical Engineering Emory University and Georgia Institute of TechnologyAtlanta 30322GAUSAand also with Joseph Maxwell Cleland Atlanta VA Medical CenterDecatur 30033GAUSA Department of Radiology and Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou 510080Chinaand also with Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and ApplicationGuangzhou 510080China

Immunohistochemistry(IHC)is a vital technique for detecting specific proteins and antigens in tissue sections using antibodies,aiding in the analysis of tumor growth and ***,IHC is costly and time-consuming,making it challenging to implement on a large *** address this issue,we introduce a method that enables virtual IHC staining directly on Hematoxylin and Eosin(H&E)***,we have developed a novel registration technique,called Bi-stage Registration based on density Clustering(BiReC),to enhance the registration efficiency between H&E and IHC *** method involves automatically generating numerous Regions Of Interest(ROI)labels on the H&E image for model training,with the labels being determined by the intensity of IHC ***,we propose a novel two-branch network architecture,called SeaConvNeXt,which integrates a lightweight Squeeze-Enhanced Axial(SEA)attention mechanism to efficiently extract and fuse multi-level local and global features from H&E images for direct prediction of specific proteins and *** SeaConvNeXt consists of a ConvNeXt branch and a global fusion *** ConvNeXt branch extracts multi-level local features at four stages,while the global fusion branch,including an SEA Transformer module and three global blocks,is designed for global feature extraction and multiple feature *** experiments demonstrate that SeaConvNeXt outperforms current state-of-the-art methods on two public datasets with corresponding IHC and H&E images,achieving an AUC of 90.7%on the HER2SC dataset and 82.5%on the CRC *** results suggest that SeaConvNeXt has great potential for predicting virtual IHC staining on H&E images.

关键词： Immunohistochemistry(IHC) Bi-stage Registration based on density Clustering(BiReC) automatic label generation SeaConvNeXt attention mechanism multi-level local and global features virtual IHC staning prediction

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Analyzing the Potential Benefits of Data Mining for Customer Relationship Management

Analyzing the Potential Benefits of Data Mining for Customer...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Aravind A R Shilpa M. Katikar Kalyan Acharjya G. Ezhilarasan Surendra Yadav Abhinav Mishra Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data mining for client dating control (CRM) is figuring out patterns and traits in customer records to inform selections about product imparting, pricing, sales, advertising and marketing, and different consumer techniques. Companies can use automated records mining strategies to outline client profiles, section clients, and better recognize purchasing conduct. Through knowledge of client conduct, corporations can target their customers greater correctly, grow revenue, and enhance customer satisfaction and loyalty. The advantages of fact mining for CRM are several, which include spotting purchaser needs and options, improving marketing effectiveness, increasing client acquisition, and boosting sales. Additionally, records mining can enable an employer to perceive lost sales possibilities, lessen operational costs, and optimize customer retention efforts. Essential data mining allows CRM-targeted organizations to recognize clients higher, optimize operations, and broaden strategically centered initiatives to improve consumer loyalty.

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Implementing Long Short-Term Memory Network for Enhancing Hyper Spectral Time Series Modeling

Implementing Long Short-Term Memory Network for Enhancing Hy...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Muktha Eti Jyoti Shekhawat Manish Nagpal Srisainath R Pranali G. Chavhan Rakesh Kumar Yadav Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai-127 Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

This take a look at proposes using lengthy quick-time period reminiscence (LSTM) networks as a tool for enhancing the accuracy of Hyper Spectral Time series (HSTS) fashions. via the use of this recurrent neural community approach, the model can account for the temporal dating among the hyper spectral spectral time collection variables. additionally, with this utility, the version can research from the spectral time series over a longer time period. This study examined the efficacy of the carried out LSTM network with the aid of comparing the results to the Hyper Spectral Time collection model performance performed prior to using this method. The effects recommend that the addition of the LSTM network accelerated the accuracy of the version through about 25%. This generation has the potential to substantially beautify the accuracy of temporal records inside the discipline of faraway sensing records analysis. In conclusion, this examine indicates that LSTM networks are a promising tool for boosting the predictive energy of HSTS models. long quick-term memory Networks (LSTM) are a type of Recurrent Neural Networks (RNNs) that have been applied to many duties in synthetic Intelligence including language information, herbal language processing, and image processing. more recently, they had been used to enhance hyper spectral time series modeling. The shape of LSTM permits it cope with and keep lengthy-term sequences of statistics, making it particularly suitable for modeling hyper-spectral time series. At whenever step, an LSTM community takes as input a vector representing the hyper-spectral values, and outputs a vector which represents the intermediate representation of the time series. these intermediate representations are then used to generate the output at every next time step. LSTM networks are for his or her ability to capture lengthy-time period dependencies, and because of their structure, they are additionally nicely-appropriate to hyper-spectral time collecti

关键词： Performance evaluation Recurrent neural networks Shape Time series analysis Predictive models Vectors Robustness

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Analysis Implementation of Chatbots to Increase Knowledge of Stunting Prevention in Indonesian Society with Bibliometric

Analysis Implementation of Chatbots to Increase Knowledge of...

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International Conference on Information Management and technology (ICIMTech)

作者： Indrajani Sutedja Lutfi Handayani Maryuni Hendry Mohammad Reza Faisal Information Systems Department School of Information Sytems Bina Nusantara University Jakarta Indonesia Faculty of Nursing and Midwifery Midwifery Study Program Binawan University Jakarta Indonesia Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Computer Science FMIPA Lambung Mangkurat University Banjarmasin Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

The healthcare industry uses AI, because it is more efficient. In Indonesia, the accuracy of measurements and transmission of children's growth and development is often wrong. Many small hospitals usually don't have length measuring equipment, so the results are inaccurate. This research aims to determine the effectiveness of the stunting prevention program in Indonesia using an information system. The methodology used is a qualitative method, to conduct subjective research and analysis. The results of the research are to improve the website/mobile application information system which can display a child's growth and development history and simple educational information related to nutrition. Digital recording methods can speed up the process of updating data with a central database in real time. The value of this research is to reach the public to utilize technology and obtain personalized and interactive information directly to individuals. The theoretical implications of this research focus on information dissemination and understanding how chatbots influence knowledge acquisition and behavior change in the context of stunting prevention can contribute to advancing these theories in the field of public health. Managerial implications can provide cost effectiveness as Chatbots offer potentially cost-effective solutions and data-driven insights where managers can leverage chatbot analytics.

关键词： Prevention and mitigation Scalability Bibliometrics Refining Chatbots Real-time systems Stakeholders Artificial intelligence Sustainable development Research and development

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Modernizing Smart Homes into Cognitive Residences

Modernizing Smart Homes into Cognitive Residences

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Gunjan Sharma K. Ranjith Singh R.Udayakumar Haider Alabdeli Kanav Krishan Azizbek Khurramov Institute of Business Management GLA University Mathura India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India CS & IT Kalinga University India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India International School of Finance and Technology Tashkent Uzbekistan

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The growing popularity of connected homes can be attributed to advances in cloud computing and machine learning. The latest technology allows devices such as smart speakers and thermostats to learn from your feedback and adapt to your personal preferences. However, these smart gadgets can also influence people's behavior. Investigate the potential impact of smart homes on behavior using a human model with hierarchical reinforcement learning. This model can be used for a variety of tasks, such as controlling humidity and temperature within a smart home under Q-learning management. We investigate possible behavioural changes and explore prospects for mutual adaptation between smart home and human models. Our human models are trained on these tasks using hierarchical reinforcement learning, which allows them to learn when to move on to new activities and how long to focus on existing activities. We then integrate human modelling with digital house models in real-world scenarios and conduct in-depth studies involving different situations in which one or more human models interact with a smart home. Our results suggest that installing a connected home may cause human models to exhibit unexpected behavior, such as frequent changes in activity or longer times to change temperature settings. What is interesting is that when two human samples are combined, some exhibit typical behavior while others reproduce the surprising results of single-subject experiments.

关键词： Adaptation models Q-learning Computational modeling Smart homes Humidity Switches Behavioral sciences Temperature control Telecommunication computing Thermostats

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Mixotrophic Chlorella pyrenoidosa biofilm with enhanced biomass production, microalgal activity, and nutrient removal from nutrient-rich wastewater

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Journal of Environmental sciences (China) 2025年 157卷 366-377页

作者： Wu, Xiongwei Jin, Cong Zhang, Chiqian Li, Ping Huang, Jim Junhui Wu, Jing Wu, Jinhua Hu, Zhiqiang School of Environment and Energy South China University of Technology Guangzhou510006 China Zhongshan Institute of Modern Industrial Technology of South China University of Technology Zhongshan528400 China Civil Engineering Program College of Engineering & Computer Science Arkansas State University JonesboroAR72467 United States Environmental Research Institute National University of Singapore Singapore117411 Singapore Department of Civil and Environmental Engineering University of Missouri-Columbia 65211 United States

Microalgae possess significant advantages in nitrogen and phosphorus removal from nutrient-rich wastewater that are highly efficient and independent of the C/N ratio. However, challenges such as low biomass productivity, high variability in nutrient removal under different trophic types, and difficulty in harvesting biomass limits the large-scale application of microalgae wastewater treatment. This study attempted to employ mixotrophic microalgae biofilm to address these issues. The biomass production, microalgal activity, and nutrient removal of Chlorella pyrenoidosa biofilms with different trophic types were compared for nutrient-rich wastewater treatment. The results showed that the biomass productivity of the mixotrophic microalgal biofilm (0.215 g/(L·d)) was 2.3, 8.6, and 6.0 times higher than that of photoautotrophic microalgal biofilm, heterotrophic microalgal biofilm, and photoautotrophic suspended microalga, respectively. Additionally, the dehydrogenase activity (DHA), indicating microalgal activity, of the mixotrophic biofilm was 2.3 and 16.5 times higher than that of photoautotrophic and heterotrophic biofilms, respectively. Meanwhile, the mixotrophic biofilm removed 96.0 % of NH4+-N and 99.2 % of PO43–-P, more efficient than that with other types of biofilms and suspended microalgae. In an open-ended air-lift photobioreactor, the mixotrophic microalgal biofilm produced biomass at 0.12 g/(L·d) and removed 90.0 % of NH4+-N and 97.6 % of PO43–-P. This study suggests that the mixotrophic microalgal biofilm shows promise in treating nutrient-rich wastewater and producing microalgal biomass for value-added products. © 2024

关键词： Microalgae

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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