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A Deep learning Technique for smart Gender Classification System 3

A Deep learning Technique for Smart Gender Classification Sy...

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3rd international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sonthi, Vijaya Krishna Sikka, Rishi Anusha, R. Devi, P. Bala Shanmuga Kamble, Sachin K Pant, Bhasker KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Sanskriti University Department of Electronics Uttar Pradesh Mathura India A. P. C. Mahalaxmi College for Women English Thoothukudi India Terna Engineering College Design Navi Mumbai India Graphic Era Deemed to Be University CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The examination of the possibility of employing deep learning techniques for transportation planning and modelling is the basis of this essay. The writers look at the transportation issues that have developed for urbanized populations. intelligent transportation systems will become increasingly crucial to satisfy people's transportation demands as the urban population grows. Automated data gathering devices demand the usage of analytics tools in order to give insights for actual decision-making and policy planning. This research has two objectives. This research begins with a review of academic literature on gender and age identification using Convolutional Neural Network-based approaches estimate, followed by a thorough examination of the present methodology. Then, using the classification techniques, we conduct a review of roughly 30 publications to draw valid conclusions and forecasts about how the various CNN approaches and transportation systems will develop in the *** to the existence of a complicated background, object occlusion, and varying lighting conditions, gender determination from facial photos is a difficult process. Face photos may be used for a variety of purposes, including tracking, identification, and expression analysis. Two deep learning-based approaches are examined in this research for facial image-based gender classification. Convolutional neural networks (CNN) and Alex Net are two examples of these techniques. The effectiveness of both models for identifying the male and female classes from face photos was tested through experiments. Results indicate that both approaches were successful in classifying people by gender. Additionally, a comparison between these two models and a few of the common techniques for gender categorization was done. © 2023 IEEE.

关键词： Eye tracking

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BOTika: A Pharmaceutical Management System Using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Clustering Algorithm

BOTika: A Pharmaceutical Management System Using Seasonal Au...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Nicole D. Bacani Dwayne Douglas C. Balitustus Christel S. Porcalla Ariel Antwaun Rolando C. Sison Jamillah S. Guialil Criselle J. Centeno College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Department Head of CISTM Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Computer Science Dean of CISTM Pamantasan ng Lungsod ng Maynila Manila Philippines Department of Information Technology Dean of CISTM Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This study introduces “BOTika: A Pharmaceutical Management System,” which incorporates the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Clustering Algorithm to tackle significant challenges faced by small and medium-sized pharmacies in Metro Manila. BOTika is designed to streamline and enhance pharmaceutical operations, focusing on areas such as inventory control, sales monitoring, profit evaluation, and medication management. Its primary goal is to improve efficiency in pharmacy operations, particularly through predictive analytics powered by the SARIMA model. The system also uses a Retrieval Report Algorithm for data retrieval and Statistical analytics for data interpretation. Additionally, a Clustering Algorithm is employed to group medicines by key characteristics, enabling targeted discounts and promotions. The system's interface, developed as a Progressive Web App (PWA), allows administrators to manage operations seamlessly, while customers benefit from a pre-order feature. Results demonstrate that the SARIMA model provides accurate demand forecasts, leading to better decision-making in pharmacy management. Ultimately, the project aims to modernize pharmacy operations, enhance customer experience, and support the sector's growth, contributing to both sustainable practices and economic progress in the region.

关键词： Clustering algorithms Pricing Predictive models Prediction algorithms Stakeholders Resource management Predictive analytics Optimization Monitoring Pharmaceuticals

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A Study on smart Green Hydrogen Production System Using IoT and AI for Small-Scale Energy Applications

A Study on Smart Green Hydrogen Production System Using IoT ...

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computing and data analytics (ICCDA), international conference on

作者： Manar Abdullah Saleh Alshiyadi Sara Ahmed Sulaiman Alfahdi Shadha Rashid Obaid Almanii Manahid Khamis Juma Alharrasi Rajesh. D. Gnana College of Computing and Information Sciences University of Technology and Applied Sciences Al Mussanah Sultanate of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

Green hydrogen is becoming more and more popular as a sustainable energy source due to the increasing demand for clean, renewable energy on a global scale. With the use of renewable energy sources, this initiative seeks to manufacture green hydrogen on a small scale via water electrolysis. A tiny appliance, such a lightbulb or a fan will be run on the hydrogen generated. The research uses IoT for realtime parameter monitoring, including temperature, voltage, water flow rate, electrolysis current, and hydrogen purity, in order to improve efficiency, safety, and scalability. Artificial Intelligence (AI)-powered algorithms will be utilized to enhance energy management and mechanize the manufacture of hydrogen. Additionally, the system will have Internet of Thingsbased safety features for automatic shutdown and leak detection. The main objective is to create a safe, effective, and intelligent small-scale hydrogen generation system with practical uses that will demonstrate the use of green hydrogen in energy solutions.

关键词： Temperature sensors Fans Voltage Green hydrogen Safety Electrochemical processes Artificial intelligence Energy management Monitoring Water resources

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Privacy Pinnacle: Improvising Healthcare data Security Through Federated Learning and Blockchain Framework

Privacy Pinnacle: Improvising Healthcare Data Security Throu...

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international conference on computing and Networking Technology (ICCNT)

作者： G Amreen Anita Kanavalli Department Of Computer Science Engineering MSRIT Bangalore Department of Information Science Engineering MSRIT Bangalore

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Developing intelligent healthcare solutions in the quickly changing world of communication technologies-driven smart cities depends heavily on data. Our work aims to protect user data, which is essential to intelligent healthcare in these kinds of urban areas, in the face of mounting worries about data privacy. However, serious privacy issues are raised by the sensitive nature of health data. Federated Learning (FL) is a potentially useful method that allows cooperative model training over several servers or devices while maintaining localized training data. However, serious privacy issues are raised by the sensitive nature of health data. In this work, we present a novel framework to improve secure healthcare data analytics in Blockchain systems by integrating FL with blockchain technology and the Interplanetary File System (IPFS). To balance privacy and data utility, our method makes use of adaptive noise distribution techniques and dynamic customization. IPFS aids in lowering the cost of data storage, while blockchain technology offers safe and transparent model update aggregation and storage. The results of our experiments show that our approach maintains excellent accuracy with 99.04% for healthcare data security analytics tasks and provides strong privacy protection against different types of attacks. Blockchain integration using Ethereum and IPFS demonstrates the usefulness and viability of our platform.

关键词： Training data privacy Federated learning data security Training data Medical services Transforms data models Blockchains InterPlanetary File System

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smart Disinfectant Dispensers with IoT Sensors and AI for Optimal Usage in Public Spaces

Smart Disinfectant Dispensers with IoT Sensors and AI for Op...

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intelligent Control, computing and Communications (IC3), international conference on

作者： R. Mohandas D. Anitha K. Balasubadra R. Aiyshwariya Devi S. Murugan M. Rajmohan Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Information Technology R.M.D. Engineering College Kavaraipettai Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and TechnologyPuduvoyal Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Ensuring hygiene in public areas is of utmost importance now that there are worldwide health concerns. This research proposes a novel approach to creating smart disinfectant dispensers fitted with Internet of Things (IoT) sensors and Artificial Intelligence (AI) to maximize their use in public areas. By using IoT technology, the system can measure disinfectant levels, detect human engagement in real time, and monitor dispenser use. Machine learning models examine the data to find the best time to restock, predict when repairs will be necessary, and personalize the dispensing amount for each user according to their habits and conditions. The system also works with cloud platforms and mobile apps for remote monitoring and data analytics, giving administrators full control and visibility. Along with lowering operating expenses and ensuring a constant supply of disinfection, this method also reduces waste. smart disinfectant dispensers build healthier communities that can withstand outbreaks by encouraging regular and thorough handwashing.

关键词： Sensor systems Real-time systems User experience Time measurement Safety Internet of Things Public healthcare intelligent sensors Remote monitoring Predictive maintenance

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Utilizing Big data for Personalized Nutrition Interventions and Health Outcomes

Utilizing Big Data for Personalized Nutrition Interventions ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Suchitra Labhane Sanjana Sharma Marwaha Swati Shastri Nihal Raj Mayank Tomar Avadhesh Kumar Koshal Department of MBA Datta Meghe Institute of Management Studies Nagpur Maharashtra India Department of Amity Institute of Social Sciences Amity University Noida Uttar Pradesh India Department of Facultyof Science Motherhood University Haridwar Uttarakhand India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The use of big data in the context of proven nutrition interventions and their effects on health issues is examined in this exploratory work. We want to tailor dietary recommendations to individual needs by leveraging big databases, which include health records, hereditary information, and healthy behaviors. Big data analytics integration makes it possible to find distinctive health connections and beneficial patterns, which in turn makes it possible to express personalized dietary recommendations that can improve health problems. Our research shows how big data may change conventional nutritional knowledge and lead to a more individualized strategy that takes specific nature and life elements into account. The results show notable improvements in the treatment of habitual conditions, the enhancement of salutary recommendations, and the promotion of general well-being. To enhance proven nutrition, this study highlights the value of interdisciplinary collaboration by fusing data wisdom, nutrition, and healthcare moxie. Refining data integration techniques and investigating the long-term benefits of supported nutrition programs on various populations are examples of potential exploration directions.

关键词： Microorganisms databases Refining Merging Medical services Big data Genetics data models Robustness Real-time systems

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IoT Applications in smart Building Energy Management

IoT Applications in Smart Building Energy Management

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Thangjam Ravichandra Madhavi Karumudi M. Uma Maheswari S. Leela Thamizhkani. B S.P. Kanniyappan Alliance School of Business Alliance University Bengaluru Karnataka India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana India Department of Mechatronics Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Civil Engineering R.M.K. Engineering College Kavaraipettai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The technologies that are a part of the Internet of Things enable smart buildings to use energy more efficiently. The main focus of the research is on the use of internet-connected sensors and devices. Real-time monitoring, analysis, and management of energy consumption are the goals of this. data is gathered from various sources, such as appliances, lighting, HVAC (heating, ventilation, and air conditioning), and other systems, employing networked systems. This offers the chance for a more effective distribution of energy. The goal of the project is to foresee energy patterns and automatically adjust settings by focussing on the integration of cloud computing, big data analytics, and machine learning algorithms. Furthermore, it highlights how the Internet of Things can help cut down on energy waste and improve sustainability by giving consumers advice and information on how to use less energy. Providing consumers with insights and recommendations helps achieve this. By promoting the adoption of more ecologically friendly energy practices, the implementation of these technologies not only reduces operating costs but also decreases the environmental impact that buildings have. The study's conclusions indicate that the field of smart buildings has advanced significantly, which may help to promote sustainability and higher energy efficiency.

关键词： Heating systems smart buildings Machine learning algorithms Lighting Ventilation Energy efficiency Real-time systems Internet of Things Sustainable development intelligent sensors

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Evolutionary computing Assisted K-Means Clustering based MapReduce Distributed computing Environment for IoT-Driven smart City

Evolutionary Computing Assisted K-Means Clustering based Map...

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international conference on computing, Communication, and intelligent Systems (ICCCIS)

作者： Srinivas, Kunal G. Hosahalli, Doreswamy Mangalore Univ Dept Comp Sci Mangalore India

ISBN: (纸本)9781728185293

In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging demands, especially towards real-time decision making, information exchange and knowledge-driven decisions. To achieve it, smart city concept which employs Internet-of-Things (IoT), distributed software computing, and Bigdata analytics has gained widespread attention. Though, inclusion of QoS-sensitive routing has helped enabling better and efficient sensory or node's data collection and dissemination;however, ensuring optimal query-driven knowledge mining and information exchange has remained a challenge. Considering it as motivation, in this paper an evolutionary computing assisted K-Means clustering algorithm is developed for MapReduce computation in Hadoop distributed framework. The proposed method employs genetic algorithm to enhance centroid estimation as well as clustering, which as a result helped in achieving better clustering to support MapReduce. The proposed GA based K-Means clustering has been applied over Hadoop-MapReduce, where to achieve aforesaid centroid estimation and clustering enhancement Silhouette coefficient was used as the objective function. Here, GA-K Means was applied in such manner that it estimates optimized centroid and clusters simultaneously over Mapper and Reducer, which makes overall computation faster and more accurate.

关键词： Bigdata analytics Genetic Algorithm Hadoop MapReduce Clustering Distributed computing smart City

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Precision Farming Using Machine Learning and data analytics

Precision Farming Using Machine Learning and Data Analytics

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Bharathwaj Nedoumaran Aashik Mathew Prosper Sundarababu Maddu Mithun P St. Joseph’s Institute of Technology Chennai Tamil Nadu India

India’s labor force and economy are highly dependent for agriculture, which accounts for 17% of the country's GDP and 50% of all jobs. The majority of farmers, however, continue to practice traditional farming practices and do not have access to modern machinery and technology. Farmers find it challenging to predict crops that will grow well in their fields because of the changing climate, which results in low yields, a reduction in food sources, and negative effects on their livelihoods. Several variables, including soil nitrogen (N), phosphorus (P), potassium (K), pH content, weather humidity, and rainfall, need to be considered, to predict the best crops to plant using computational tools. In order to do so, we used crop, soil, and weather datasets along with six machine learning algorithms (Naive Bayes, Decision Tree, Logistic Regression, Support Vector Machine, Random Forest, and XGBoost). We implemented the XGBoost algorithm in a web application using Flask, which has the highest accuracy of 99.31%. The application enables farmers to quickly access and input data to forecast the best crops for their fields.

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Assessing Wildlife Tourism's Role in Conservation Awareness Using Artificial Intelligence and data Science for Sustainable Tourism Development

Assessing Wildlife Tourism's Role in Conservation Awareness ...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Suvarna Sathe Mahesh Randhave Manasi Sadhale Aditi Joshi Amit Khare Nilesh Upadhye Tilak Maharashtra Vidyapeeth Pune

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The purpose of this exploration paper is to improve the development of sustainable tourism by examining the intersection of wildlife tourism and conservation consciousness through the perspective of artificial intelligence (AI) and data wisdom. Through the use of data analysis and tools driven by artificial intelligence, the project analyzes how wildlife tourism practices might be optimized to strengthen conservation efforts. Through the utilization of artificial intelligence methodology for caller shadowing, wildlife monitoring, and predictive analytics, the investigation investigates how these technologies can serve to mitigate adverse effects on wildlife while simultaneously fostering beneficial conservation concerns. Additionally, it investigates how tailored gestures and interactive instructional aids, powered by artificial intelligence, can help excursionists become more conscious. In addition to this, the paper discusses the difficulties that are related to ethical considerations, the sequestration of data, and the incorporation of unique information. This paper gives insight into how artificial intelligence (AI) and data wisdom can be effectively applied to support sustainable wildlife tourism and advance conservation pretensions. This insight is provided through case studies and tactics that are driven by data (data-driven). The purpose of the findings is to use them to inform policy and functional practices to further promote tourism that is both responsible and meaningful.

关键词： Ethics Soft sensors Wildlife data science Robustness Stakeholders Artificial intelligence Sustainable development Monitoring Shadow mapping

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