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IEEE Future Networks World Forum (FNWF)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Houbing Song Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Department of Electrical & Electronic Engineering Science University of Cyprus Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Information Systems University of Maryland Baltimore Country Baltimore MD USA

In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.

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Skin Cancer Classification using Machine Learning Techniques

Skin Cancer Classification using Machine Learning Techniques

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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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The Design of IR System for PC Systems

The Design of IR System for PC Systems

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Jason Gunjan Chhabra Mohammed Al Sailh France Laith H. Jasim Abdul Redha Hussein Sabr Doaa Talib Zaidan Department of computer applications Hindustan institute of technology and science Department of CSE Graphic Era Hill University Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Tareq Hafdhi Abdtawfeeq Dhi Qar Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Department of Laser and Optical Electronics Engineering Kut University College Al Kut Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Collaborative robotics, in conjunction with artificial intelligence (AI), offers a contemporary and effective paradigm for secure machine-human interactions. This synergy branches out into a variety of industries, including education and entertainment, in addition to industrial uses. Two intriguing board games that offer a platform for examining the potential of cooperative robotic systems are chess and checkers. An intelligent and cooperative robotic system designed for use in Italian checkers games is described in this context by the study being presented. To record the game state, the gadget employs a camera. To physically move pieces across the board, a pick-and-place mechanism is used. An algorithm is used to automatically choose the optimal moves that comply with the rules. The system respects the kinematic restrictions of the manipulator while optimizing minimum-time trajectories live for every manipulation, guaranteeing a smooth and dynamic gaming experience. An experimental validation employing a seven-degree-of-freedom Franka Emika arm verifies the effectiveness of the proposed approach in real-world settings.

关键词： Service robots Heuristic algorithms Robot vision systems Collaboration Entertainment industry Games Kinematics

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Majority Voting-based Consensus Mechanism for UAVs Decision Making in Battlefield

Majority Voting-based Consensus Mechanism for UAVs Decision ...

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International Conference on computer, Information and Telecommunication Systems (CITS)

作者： Riya Kakkar Rajesh Gupta Mohammad S. Obaidiat Nilesh Kumar Jadav Sudeep Tanwar Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Distinguished Professor at the King Abdullah II School of Information Technology The University of Jordan Jordan School of Computer and Communication Engineering University of Science and Technology Beijing China The Amity University Noida Uttar Pradesh India

The revolutionary and rapid development of Information and Communication Technology (ICT) has significantly contributed to modernizing military operations by improving the long-range communication and information sharing of unmanned aerial vehicles (UAVs) on the battlefield. However, efficient decision-making between clusters of UAVs controlled by the controller station (CS) is still one of the critical aspects that can cause potential threats to the armed forces on the battlefield. Thus, we have proposed a majority voting-based mechanism to attain consensus decision-making for UAVs, i.e., followers, monitored by the CS (leader) on the battlefield. Furthermore, we have focused on various voting scenarios to choose the efficient decision (attack or retreat) for the armed forces without any conflict between leader and follower utilizing the majority voting mechanism. Finally, the performance evaluation of the proposed mechanism is analyzed and simulated using the performance metrics of consensus delay and throughput.

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Economic indicators evaluation to study the feasibility of a solar agriculture farm: A case study

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Solar Compass 2024年 10卷

作者： Gautam, Shivani Das, D. Bhagwan Saxena, Ajay Kumar Lecturer at Department of Physics and Computer Science Faculty of Science Dayalbagh Educational Institute (Deemed to be University) UP Dayalbagh Agra 282 005 India Professor at Department of Electrical Engineering Faculty of Engineering Dayalbagh Educational Institute (Deemed to be University) UP Dayalbagh Agra 282 005 India Professor Emeritus at Department of Electrical Engineering Faculty of Engineering Dayalbagh Educational Institute (Deemed to be University) UP Dayalbagh Agra 282 005 India

The 209 kWp solar agricultural farm at Dayalbagh Educational Institute's Dairy Campus in Agra, India, is the subject of this economic analysis. This study analyzes economic indicators relevant to agriculture food production, such as gross financial margins, farm profits, and cost-benefit ratios. Other measured variables include NPV (net present value), PB (payback period), and LCOE (levelized cost of electricity) of solar power generation to determine the economic viability of this agrivoltaics (production of solar energy and agriculture on the same land) APV project. Farm profit and gross financial margins both are positive values of 161, 907 INR (Indian Rupees) and 316, 907 INR, respectively. The cost-benefit ratio was *** economic factors of solar energy generation were studied for two systems: surface-mounted or ground-mounted solar systems and an elevated solar system with agriculture production on the same ground (APV or agrivoltaics). Both systems had the same production capacity. Two different hypothetical situations were used for each system. In the first situation (Case Study A), the assumption was that solar power performance would remain constant during operation. In the second situation (Case Study B), the assumption was that annual performance would be reduced by 0.5 %.The results show that APV is better than the surface-mounted system because there is no significant difference between the payback periods of APV and surface-mounted systems;the NPV of APV is greater than the surface-mounted solar system for both the study case A and case B and the LCOE of APV is 55 % less than the LCOE of the surface-mounted solar system. © 2024 The Author(s)

关键词： Agriculture Agrivoltaics Economics Solar

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Handling tactful data in cloud using PKG encryption technique

Handling tactful data in cloud using PKG encryption techniqu...

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4th Smart Cities Symposium (SCS 2021)

作者： B.C. Gajarla A.V. Rebba K.S. Kakathota M. Kummari S. Shitharth Student Department of Computer science and EngineeringVardhaman College of Engineering Assistant Professor Department of Computer science and EngineeringVardhaman College of Engineering

With the availability of the cloud storage services, users can store data to the cloud and have the access to share the data to others. The cloud file may contain some sensitive information, this information shouldn't be exposed to others. The possible solution could be to encrypt the whole file which is shared before sending it to the cloud and to generate the signature to verify the encrypted file. Then, upload the encrypted file and signatures to the cloud. This enables the sensitive information to be private and only the owner can decrypt the file. Hence, it is not feasible to use by others. Distributing the decryption is the possible way to access it by others. However, it is not the efficient solution to implement this method in real scenarios. In order to overcome this approach, an efficient solution is implemented. In this new approach, sensitive information is kept private and can easily execute without any complications. Here, a sanitizer is used to sanitize the block which contains the sensitive information of the file. Firstly, the user binds the data blocks with respect to the sensitive information of the original data and generates a digital signature to that block and then sends it to the sanitizer. The sanitizer filters these data blocks and also sanitizes the data corresponding to the sensitive information. It also evaluates the corresponding signatures into valid ones. The signature is used to prove that the cloud truly possesses the data blocks. The third-party auditor is a public verifier, it verifies the data stored in the cloud on behalf of the user. This method supports the data sharing so that the sensitive information is protected in the cloud.

关键词： cryptography data integrity digital signatures storage management cloud computing

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Cloud-Powered Healthcare Appointment Optimization with Reinforcement Learning for Efficiency

Cloud-Powered Healthcare Appointment Optimization with Reinf...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： S. Mohan Kumar John Benito Jesudasan Peter S. Kolangiammal Azath Mubarakali S. Karthik S. Sujatha Professor and Director Research and Innovations CMR University Bangalore India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Improving efficiency and patient satisfaction through better appointment scheduling is a problem for healthcare systems across the globe. To improve healthcare appointment scheduling procedures, this research proposes a new method that makes use of cloud computing and reinforcement learning (RL) algorithms. to maximize healthcare provider efficiency while minimizing patient wait times, resource usage, and operational costs by dynamically learning and adapting scheduling rules using RL, taking use of the scalability and computing capacity of cloud infrastructure. To train RL agents, create a simulation environment that mimics real-world healthcare conditions. Our findings show that RL-based scheduling strategies are more efficient at scheduling appointments than conventional techniques, and we prove it via rigorous testing and assessment. Additionally, we demonstrate how our strategy can handle dynamic healthcare contexts with different patient numbers and resource restrictions, demonstrating its flexibility and resilience. The results show that RL approaches run by the cloud can change healthcare appointment scheduling for the better, leading to healthcare systems that are more responsive and flexible. Utilizing the complementary strengths of cloud computing and RL, our method provides a data-driven, scalable solution to the hard problem of healthcare schedule optimization, which improves both patient care and operational efficiency

关键词： Cloud computing Technological innovation Schedules Job shop scheduling Processor scheduling Medical services Optimal scheduling Reinforcement learning Dynamic scheduling Testing

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IoT-Enabled Smart Manhole Management System for Real-time Status, Water Level, and Gas Detection

IoT-Enabled Smart Manhole Management System for Real-time St...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Md Asif Imran S M Masfequier Rahman Swapno Gunjan Chhabra Keshav KaushiK Md. Arif Anzum Mahi Md Babul Islam Rezaul Haque Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Adjunct Professor Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Dept. of Computer Modeling Electronic and System Engineering UNICAL Rende Italy Applied Bahari Big Data Research House Dhaka Bangladesh Department of Computer Modeling Electronic and System Engineering UNICAL Rende Italy

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Regular monitoring of the maintenance manhole system is imperative to uphold the city's health and cleanliness standards. Our study offers an intelligent manhole that monitors the manhole's condition, water level, and gases. We tested the status, gas, and water levels of our revolutionary, innovative manhole in several places. Our system's Blink app position tracking of our smart manhole is critical. This capability improves monitoring efficiency and speeds up issue resolution. Our work revolutionizes municipal utility maintenance and safety by optimizing manhole performance. Integrating a Node MCU, GPS module, gas sensor, ultrasonic sensor, and Android Blynk software creates a complete real-time monitoring and data-collecting system. Combining these technologies, our Manhole Monitoring System improves safety and streamlines maintenance, making municipal infrastructure more robust and efficient. This paper highlights the promising features of IoT and shows how it may be used to improve the smart manhole. In the future, this creative method may result in a more durable and better intelligent city.

关键词： Surveillance Urban areas Robot vision systems Transforms Cameras Real-time systems Maintenance

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Transportation Planning Decision-Making Using Intuitionistic Fuzzy Programming

Transportation Planning Decision-Making Using Intuitionistic...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Pinki Gulia Vikrant Sharma Manik Rakhra Kapil Jairath Sakshi Suri Rakesh Kumar Department of Mathematics Lovely Professional University Phagwara Punjab India Computer Science and Engineering Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand India Department of computer science and Engineering Lovely professional University Phagwara Punjab India Department of Sciences Trinity College Jalandhar Punjab India School of design-II Interior & Furniture design Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In this research, we use intuitionistic fuzzy programming to bring insight into transportation planning decision-making processes, namely multi-objective optimization (MOO). Considerations for transportation time and prices are made in the case study, which entails moving things from one location to another. In order to improve the model’s capacity to deal with uncertainty, the supply and demand parameters are represented using crisp parameters. With the goal of simultaneously minimizing time and overall cost, the study interacts MOO with crisp data. Our solution is shown to be feasible by means of a hypothetical numerical issue. With linear membership as well as non-membership functions in particular, the transportation planning problem is tackled and solved. As a result of offering decision-makers with thorough solutions that successfully balance many objectives, the results highlight the effectiveness of the intuitionistic fuzzy programming technique in addressing real-world transportation planning difficulties.

关键词： Uncertainty Supply and demand Decision making Transportation Programming Linear programming Planning Numerical models Informatics Optimization

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Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four Switch Induction Motor Drive

Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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