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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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Use of IOT in computer Control Using Hand Gesture Using AT Mega 328 over the Cloud

Use of IOT in Computer Control Using Hand Gesture Using AT M...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Ishwarlal Rathod Kunal Rathod Neha Gupta Computer Science and Information Technology Symbiosis University of Applied Sciences Indore India Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

In the realm of remote control, my preference is computer manipulation through hand gestures. Recent strides in technology showcase the feasibility of using manual gestures on Arduino platforms, facilitated by cloud computing. A Wi-Fi-based hand gesture computer control system was developed using an ESP-01 Wi-Fi module and an Arduino microcontroller, enabling remote control and monitoring without geographical constraints or time limitations. The ESP8266 Wi-Fi module played a crucial role in establishing wireless communication with an Android application (S Remote) for transmitting commands to regulate domestic appliances. The AT mega 328 AVR series Microcontroller R3, lacking built-in Wi-Fi, required the ESP-01 Wi-Fi module for wireless communication and programming. The Arduino IDE facilitated programming, incorporating ESP-01 and LCD components. Hand gesture-controlled robots and PC/laptop hand gesture control systems, both utilizing Arduino technology, exemplify similar applications. The Human Machine Interface (HMI) concept involves physical components and software enabling communication between users and machines. HMI devices, including LED indicators and touch screens, traditionally use keyboards and mice. This research implemented a hand gesture controller using Arduino technology, specifically for web browser features, video playback, and VLC media player audio adjustments. The ESP-01 Wi-Fi module, configured via AT instructions through a UART serial connection, played a key role in integrating a hand gesture system for effective computer control. Users can perform actions like playing or pausing videos and adjusting audio amplitude within the VLC media player solely through manual gestures.

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Enhancing Data Security and Data Sensitivity: Leveraging the Synergy of Blockchain Artificial Intelligence

Enhancing Data Security and Data Sensitivity: Leveraging the...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Vikas Jain Sachin Chouhan Vandana Kate Nidhi Nigam Shilpa Bhalerao Dept of Computer Science and Information Technology Acropolis Institute of Technology & Research Indore India

Data security and sensitivity have become significant challenges for organizations worldwide in the digital transformation era. Protecting sensitive user data from cyber-attacks, breaches, and information exposure is of utmost concern. This research paper explores the potential of integrating blockchain, artificial intelligence (AI), and machine learning (ML) technologies to enhance data protection and ensure confidentiality. The introduction provides an overview of data sensitivity and the growing importance of data security in the digital age. It delves into blockchain technology, emphasizing its decentralized, immutable ledger for secure authentication, access control, and tamper-proof data storage. The paper presents various use scenarios, illustrating how blockchain safeguards sensitive user data, including medical records, financial information, and personal identifiers. It further examines AI's role in data security, showcasing its capability to detect threats, analyze anomalies, and authenticate users, thereby blocking unauthorized access to confidential information. Additionally, the study explores machine learning for data sensitivity management, highlighting its prowess in formulating access control policies and data categorization. The research underscores how blockchain, AI, and machine learning synergize to create a comprehensive data security architecture, enabling businesses to leverage the immutability of blockchain and the intelligent data protection of AI/ML. Real-world examples demonstrate the effectiveness of this synergy in enhancing data security and safeguarding against modern threats.

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Healthcare Assistance to COVID-19 Patient using Internet of Things (IoT) Enabled Technologies

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Materials Today: Proceedings 2023年 80卷 3777-3781页

作者： Mukati, Naveen Namdev, Neha Dilip, R. Hemalatha, N. Dhiman, Viney Sahu, Bharti Department of Electronics and Communication Engineering Prestige Institute of Engineering Management and Research Indore India Department of Electronics and Instrumentation Engineering Shri Govindram Seksaria Institute of Technology and Science Indore India Acharya Institute of Technology Department of Mechatronics Engineering Bengaluru India Chennai India Postgraduate Institute of Medical Education and Research Chandigarh India Department of Computer Engineering Dr. D.Y. Patil Institute of Technology Pune18 Pimpri India

The IoT can lead to disruptive healthcare innovation. research articles on IoT in healthcare and COVID-19 pandemics are thus researched in order to discover the potential of this technology. This literature-based research may help professionals to explore solutions to associated issues and battle the COVID-19 epidemic. Using a process diagram, IoT's significant accomplishments were briefly evaluated. Then seven critical IoT technologies that look useful in healthcare during the COVID-19 Pandemic are identified and illustrated. Finally, in the COVID-19 Pandemic, potential fundamental IoT applications were identified for the medical industry with a short explanation. The present predicament has opened up a fresh avenue to creativity in our everyday lives. The Internet of Things is an up-and-coming technology that enhances and gives better solutions in the medical area, such as appropriate medical record-keeping, sample, device integration, and cause of sickness. IoT's sensor-based technology gives a remarkable ability to lower the danger of intervention in challenging circumstances and is helpful for the pandemic type COVID-19. In the sphere of medicine, IoT's emphasis is on helping to treat diverse COVID-19 situations accurately. It facilitates the work of the surgeon by reducing risks and enhancing overall performance. Using this technology, physicians may readily identify changes in the COVID-19′s vital parameters. These information-based services provide new prospects for healthcare as they advance towards the ideal technique for an information system to adapt world-class outcomes by improving hospital treatment systems. Medical students may now be better taught and led in the future for the identification of sickness. Proper use of IoT may assist handle several medical difficulties such as speed, affordability, and complexity appropriately. It may simply be adapted to track patients' calorific intake and therapy with COVID-19 asthma, diabetes, and arthritis. In C

关键词： COVID-19

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A Comprehensive Machine Learning Based Ensemble Model for Prediction of Possible Diseases and Recommending Medicines

A Comprehensive Machine Learning Based Ensemble Model for Pr...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Priyanka Jangde Kamal Kumar Sethi Narendra Pal Singh Praveen Bhanodia Durgesh Mishra Computer Science and Engineering Acropolis Institute of Technology and Research Indore India Symbiosis University of Applied Sciences Indore India

Many instances remain untreated globally due to the lack and unavailability of medical services. In this regard, efficient monitoring and analysis of medical records will be helpful in timely identification and prediction of diseases. To enable a quicker and more precise diagnosis, a platform that keeps track of a patient's medical history and makes illness predictions based on the patient's present symptoms is needed. Early disease prediction allows users to determine the severity of a condition and take appropriate action timely. Thus, anticipation of illness in its early stage becomes essential but it's difficult for doctors to anticipate outcomes precisely and accurately based only on symptoms. Availability of medical data and its analysis with the help of machine learning techniques can help the healthcare system for appropriate predictions and recommendations of diseases. In this paper, based on the patient's symptoms and medical records, a general illness prediction system is proposed. Using the input image for disease prediction, we applied an ensemble model exploiting the KNN, SVM, Naïve bayes and deep learning techniques to accurately predict diseases. The proposed model superseded the existing techniques with 98% accuracy on experimental study performed using benchmark datasets.

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An Anatomization for Classification Skin Lesion Using Custom CNN Framework 1

An Anatomization for Classification Skin Lesion Using Custom...

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1st International Conference on Industrial Electronics research and Applications, ICIERA 2021

作者： Gupta, Shubham Acropolis Institute of Technology and Research Department of Computer Science Indore India

ISBN: (纸本)9781665435420

A skin lesion is a growth or appearance anomaly in the dermis that is different from the rest of the body. A tiny scratch or something more serious like skin cancer might be completely unnoticeable. The use of deep learning (DL) architectures for image categorization in several fields, including dermatology, has shown impressive results. The expectations produced by DL for applications like image-based diagnostics have necessitated that non-experts become acquainted with algorithms' working principles. Obtaining hands-on experience using these tools through a simplified however realistic method, in our opinion, may significantly aid in their intuitive comprehension. Even students without a strong mathematical background may understand concepts like convolution by seeing the outcomes of DL algorithms' operations on dermatological images. Furthermore, the ability to fine-Tune hyperparameters even computer code expands the reach of basic intuitive understanding of these methods without having complex computational theoretical skills. It is now feasible because of recent technology advancements that have lowered technical technological obstacles associated with usage of these tools, creation them available to a larger population. So, we present a custom CNN for training a CNN on a dataset that contains skin lesions images linked by distinct skin cancer groups. Activity is open-source doesn't need any additional software to be installed to use it. Additionally, we describe the algorithm and its functions in detail, starting with the creation of the basic computer code building blocks and leading the reader through the execution of real-world examples, comprising visualization assessment of results. We get a training accuracy of 100 percent a validation accuracy of 98.32 percent using this method. © 2021 IEEE.

关键词： Dermatology

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GAN, CNN and ELM Based Breast Cancer Detection 2

GAN, CNN and ELM Based Breast Cancer Detection

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2nd International Conference for Innovation in technology, INOCON 2023

作者： Kate, Vandana Ushasree, R. Tharsanee, R.M. Kukreja, Taranjeet Saraf, Swarnlata Varadharajan, Bhoopathy Acropolis Institute of Technology and Research Madhya Pradesh Indore India Dayananda Sagar Academy of Technology and Management Karnataka Bangalore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Tamilnadu Erode India University Institute of Pharmacy Pandit Ravishankar Shukla University Chhattisgarh Raipur India Malla Reddy College of Engineering Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350320923

The most prevalent kind of cancer among females is known as breast cancer. One of the most significant challenges facing modern medicine is improving the accuracy of breast cancer screenings. In addition to lowering costs associated with treatment, accurate diagnosis of cancer, regardless of whether it is benign or malignant, is of utmost importance. Image processing is a tool that can detect breast cancer in its early stages without causing any damage. The processing of images comes with a number of inherent uncertainties that can be caused by a variety of factors, including the sampling to noise ratio, the initial digitalization, the intensity, and the special domain. We put the Extreme Learning Machine (ELM) to work classifying, identifying, and instructing. This research suggests a GAN-CNN-ELM-based technique for detecting breast cancer. This method produces an accuracy of about 99% which outperforms the GAN-CNN model. © 2023 IEEE.

关键词： Diseases

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Transforming Classroom Instruction: Embracing Machine Learning-Assisted Teaching Pedagogy 3rd

Transforming Classroom Instruction: Embracing Machine Learni...

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Bhatt, Vinod Gupta, Dev Brat Jain, Divya Jain, Manisha Mathew, Geethu Anne Sirohi, Gajendra School of Advanced Sciences and Languages VIT Bhopal University Madhya Pradesh Bhopal India Jaypee University of Engineering and Technology Guna India National University of Science and Technology Muscat Oman Department of Commerce Acropolis Institute of Management Studies and Research Indore India

ISBN: (纸本)9789819727155

The quality of teaching in the classroom significantly affects students’ learning results, highlighting its critical role in education. One progressive teaching approach involves the use of machine learning, demonstrating a major technological influence on educational methods. This approach mixes machine learning principles to enrich learning spaces and teaching methods. By applying machine learning tools, educators can tailor teaching methods, delve into student data to tackle specific educational needs, and craft engaging learning activities. These machine learning tools help teachers enhance how they instruct. These machine learning techniques enrich the educators’ methods of instruction as they can be employed to fasten machine learning based teaching strategies in K-12 classrooms. They can pinpoint the weak areas, then create the lessons that are adjusted, and finally direct the help to where it is really needed the most. Integration of a game-like aspect and interactive approach makes learning more involving and enjoyable. These methods invite students to interact with learning from one’s own experience. The advantages of this teaching strategy are the enhancement of the teachers’ content delivery and the development of lessons’ design. Through machine learning, teachers can produce and distribute learning documents in a short time that meet the individual needs of learners. Immediate feedback and assessment are the qualities, which natural language processing makes possible, and serve teachers in finding the best ways of teaching. However, along with machine learning-backed training, a collaborative approach among instructors is also endorsed. Through automation of the repetitive jobs, teachers can now devote more energy to classroom instruction and student support. Data empowering insights provide teachers with valuable help to become successful in their teaching with the confirmation that the educational methods used are quite effective. In addition, techno

关键词： Machine learning

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From Stress to Success: Leveraging Tracebility and Workability to Support Student Well-Being in Higher Education

From Stress to Success: Leveraging Tracebility and Workabili...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Leeladhar Chourasiya Sushma Khatri Kamal K Sethi Anita Mahajan Avani Bhawsar Tanmay Verma Neeraj Sharma Computer Science and Engineering Acropolis Institute of Technology and Research Indore India Information Technology Vasantdada Patil Prasthan College of Engineering and Visual Arts Mumbai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The research explores the vital role of traceability in addressing student stress within the education sector. It underscores the importance of systematically tracking and analyzing stress factors and their impacts on students over time. The study aims to develop a framework for capturing, analyzing, and responding to stress-related data in educational settings. This involves understanding student behavior and engagement through machine learning and computer vision technologies. Data collection methods include Google Forms and consultations with psychologists. The document outlines model development, training, real-time monitoring, data visualization, user interaction, and intervention strategies. Furthermore, it elucidates the concepts of traceability and workability in the context of AI, addressing challenges and strategies for overcoming them. The paper also delves into stress-related issues in education and potential solutions.

关键词： Workability Data visualization Human factors Mental health Machine learning Real-time systems Data models Cultural differences Global communication Socioeconomics

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Samarjini - Revolutionizing Household Cleaning with an Affordable IoT-Based Automatic Mopper

Samarjini - Revolutionizing Household Cleaning with an Affor...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Siddharth Joshi Vandana Kate Shilpa Bhalerao Prasoon Tripathi Sudhanshu Sharma Dept. of Computer Science and Information Technology Acropolis Institute of Technology and Research Indore India

The title “Samarjini” draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that “Samarjini” excels in floor cleaning, adeptly avoiding obstacles and autonomously navigating through spaces.

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