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检索条件"机构=Departments of Computer Science and Engineering and Electronics and Communication Engineering"

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Modelling of 7-Segment Hex Decoder using Silicon Microring Resonator

Modelling of 7-Segment Hex Decoder using Silicon Microring R...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Raju, Kommisetti Murthy Nirmala Bai, K.S. Mondal, Bikas Roy, Bhaskar Banerjee, Sudipta Kundu, Sumanta Electronics and Communication Engineering Shri Vishnu Engineering College for Women Bhimavaram India Electronics and Communication Engineering Jntua College of Engineering Kalikiri Kalikiri India Electronics and Computer Science Narula Institute of Technology Kolkata India Asansol Engineering College Asansol India Pune India Electrical Engineering Narula Institute of Technology Kolkata India

ISBN: (纸本)9798331540821

A creative design for a 7-segment hex decoder that makes use of an optical microring Resonator (OMRR). Digital devices called decoders are used to transform binary input into the required output format. The hexadecimal is widely used in programming languages, memory addressing, error codes, etc. The main advantage of 'Hexadecimal' numerals is that they require less memory to store more numbers. After processing the binary code, the decoder accepts it and converts it back into a format that output devices can understand. The entire design has been simulated step-by-step in MATLAB. An all-optical decoder with a quick response time can significantly enhance various aspects of digital data communication and processing. The microring resonator is modulated utilizing an optical pump signal, which is the powerful optical laser beam. This microring resonator-based optical decoder can be applied to optical communication and networking, as well as optical secure coding circuits, as a basic building block. © 2024 IEEE.

关键词： Laser beams

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Exploring the Impact of Node Velocity on communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Artificial Intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network 1

Artificial Intelligence powered chatbots for the speech disa...

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1st IEEE International Conference on Advances in Computing, communication and Networking, ICAC2N 2024

作者： Mukherjee, Subhayan Chatterjee, Anusuya Chandrakar, Khushbu Saxena, Sandeep Asansol Engineering College Artificial Intelligence and Machine Learning Asansol India Asansol Engineering College Electronics and Communication Engineering Asansol India Asansol Engineering College Computer Science and Engineering Asansol India Greater Noida Institute of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350356816

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled 'Artificial Intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network' provides a novel integration of Artificial Intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities. © 2024 IEEE.

关键词： Assistive technology

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Artificial Intelligent based Cost Optimized Smart Energy Distribution and Management 2

Artificial Intelligent based Cost Optimized Smart Energy Dis...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Anitha, R. Siva Prasad, G. Senthil, M. Sarfaraz Ahmed, A. Raja, Laxmi Vijayakumar, S.D. Government Engineering College Department of Electronics and Communication Engineering Kerala India QIS College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh India Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karpagam Academy of Higher Education Department of Cyber Security Coimbatore India Builders Engineering College Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798331534950

The ever-increasing need for energy, along with the requirement to cut emissions of greenhouse gases and maximize efficiency in energy use, has been a driving force behind the development of cutting-edge technology in the field of intelligent energy distribution and management. This research article proposes an Artificial Intelligence (AI)-based cost-optimized smart energy distribution and management system. The purpose of this research is to present a new solution to addressing these difficulties. In order to intelligently manage the distribution and consumption of energy resources in a manner that is both cost-effective and efficient, the system that has been developed makes use of several artificial intelligence approaches, such as machine learning, optimization algorithms, and data analytics. The system's goal is to maximize energy utilization while keeping costs to a minimum and achieve the best possible optimization of energy distribution. In order to forecast energy demand and supply, the AI algorithms analyses data from the past as well as data that is being collected in real time. They also take into account aspects such as the current weather, the current time of day, and the preferences of individual users. The system makes real-time adjustments to the distribution of energy based on these projections, taking into account issues such as load balancing, the integration of renewable energy sources, and grid stability. Additionally, the system supports demand response capabilities, which enables users to actively participate in the management of energy by modifying their patterns of consumption based on real-time pricing signals. This allows users to actively participate in the management of energy. This demand-side management function not only promotes energy efficiency but also gives customers more agency in terms of the decisions they make regarding their energy consumption. © 2024 IEEE.

关键词： Artificial Intelligence Cost Optimization Demand Response Energy Distribution Energy Management Machine Learning Optimization Renewable Energy Smart Energy

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A Hybrid Approach for Diagnosing Diabetic Retinopathy in Retinal Fundus Images using an Adaptive Canny Edge Detection approach 15

A Hybrid Approach for Diagnosing Diabetic Retinopathy in Ret...

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

作者： Manjula Devi, R. Kowsalya, M. Dinesh, P.M. Ponmurugan, P. Berlin, A. Kavin Kumar, K. Sangeetha, M. Chandrasekaran, Gokul Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Sona College of Technology Salem636005 India Chennai600078 India Department of Electronics and Communication Engineering Jansons Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Kongu Engineering College Perundurai638060 India Department of Information Technology Dr.N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798350370249

Individuals diagnosed with diabetes are susceptible to developing diabetic retinopathy (DR), a degenerative eye condition that affects the retina. When blood vessels in the retina get damaged, it can cause permanent vision impairment or even blindness if the underlying condition is not addressed quickly enough. Early diagnosis and treatment of this disease can save a patient's eyesight and prevent blindness. Successful medications for DR are available, but people with diabetes need to be identified and monitored early on. In order to diagnose diabetic retinopathy, many physical examinations have to be conducted;however, these types of exams require more time to complete. Adaptive Canny Edge Detection Training using Adaptive Skipping technique (ACEDIAS) is used in this research to speed up the detection process of diabetic retinopathy by detecting the edge and boost diagnostic accuracy. The study team chooses to use a 2063-image dataset compressed from Kaggle's library. The suggested ACEDIAS model is evaluated for its performance using the measures such as accuracy, recall, precision, and F1-score. It has been demonstrated through simulation that the suggested ACEDIAS model outperforms the existing models. ©2024 IEEE.

关键词： Ophthalmology

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Offset-Fed Defected Ground Structure (DGS)-Based Metamaterial-Inspired Asymmetric SRR for 5G Terminal Applications 3rd

Offset-Fed Defected Ground Structure (DGS)-Based Metamateria...

引用

3rd International Conference on Computational electronics for Wireless communications, ICCWC 2023

作者： Meenambal, B. Vasudevan, K. Uma Maheshwari, G. Sherene, J. Department of Electronics and Communication Engineering Thiagarajar College of Engineering Tamil Nadu Madurai625015 India Department of Computer Science and Engineering Kamaraj College of Engineering and Technology Tamil Nadu Virudhunagar626001 India

ISBN: (纸本)9789819719426

In this research, a low-profile metamaterial-inspired dual-band antenna is proposed for 5G communication which covers n78 band of Laptop and Mobile Terminal applications and 5G IOT Wireless Avionics Intra communication (WAIC) applications. The designed antenna consists of rectangular Split Ring Resonator (SRR) as radiating element and partial rectangular ground plane with defected ground structure (DGS) is achieved by Complementary Split Ring Resonator (CSRR). It has been designed on Rogers RO4350 substrate which has a thickness of 0.8 mm, with the relative dielectric constant value Ɛr = 3.66 and with the loss tangent value tan δ = 0.004. The results indicate that metamaterial structure improves the gain, bandwidth and radiation efficiency of the antenna. The simulated 10 dB impedance bandwidth covers 5G-n78 band with the center frequency of 3.5 GHz and WAIC band with the center frequency of 4.3 GHz. The proposed antenna operates with high radiation efficiency, better gain and covered required bandwidth with linear polarisation. A reduced dimension of 0.25λ0 × 0.35λ0 × 0.009λ0 is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G mobile communication systems

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Clustering by Improved PSO based Jaya Algorithm for Energy Optimization of Wireless Sensor Networks 5

Clustering by Improved PSO based Jaya Algorithm for Energy O...

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5th IEEE International Conference on Electrical, computer and communication Technologies, ICECCT 2023

作者： Malarvizhi, P. Kavithaa, G. Computer Science and Engineering Knowledge Institute of Technology Salem India Electronics and Communication Engineering Government College of Engineering Salem India

ISBN: (纸本)9781665493604

In recent years, industries have automated processes which mean the amount of human participation has decreased, resulting in the Fourth Industrial Revolution. A highly distributed self-organizing system known as a Wireless Sensor Networks is employed in so many control systems such as monitoring the surroundings, automate the reporting, and detecting the event. High bandwidth needs, high power consumption, security and quality of service delivery are some of the obstacles that wireless sensor networks must overcome. Each sensor node in a wireless sensor networks has a different power consumption rate based on the non-uniformity of event detection and the interspace between the sink node and sensor node. This shortens the lifespan of the network and causes an energy difference between the sensor nodes. Particle Swarm Optimization based Jaya algorithm (PSO-J) has been experimented to lower the power consumption of the sensor node by improving the selection of cluster head. The proposed algorithm provides better results than existing clustering algorithms. © 2023 IEEE.

关键词： Sensor nodes

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Predictive Healthcare with Integrated EEG and Facial Emotion Recognition using Deep Learning and Transfer Learning

Predictive Healthcare with Integrated EEG and Facial Emotion...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mahato, Shashank Saranyaraj, D. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350361537

The main objective of this innovative project is to transform modern-day neurological healthcare, additionally pairing and synchronizing EEG signals with facial recognition information for efficient detection of neurological sicknesses. Machine learning algorithms are used for promptitude, ensuring rapid interventions during the course of the disease, and for careful and efficient balancing of the patient's conditions. The system based on the above-discussed approach is characterized by a patient-centered nature ensuring that trust and ethical liability are the top priorities of the developed model. Such system supported by multiple machine learning algorithms also increase the accuracy of the diagnosis, its potential for early identification, and targeted therapy, ultimately having a beneficial effect on the public health. The researches findings demonstrate that there are convincing proofs that machine learning can be used for predictive healthcare: VGG19 achieved 86.28%, Random Forest Classifier 94.48%, and for epilepsy, Random Forest and Gradient Boosting for brain tumors 92.12% and for Parkinson's disease 90.52%, respectively. Ensembled EEG analysis had the accuracy of 91.83%. Importantly, a result of 82.51% accuracy was achieved with the final decision-making process using transfer learning with the method of ensembled models and VGG19. Thus, it is possible to conclude that different machine learning algorithms are effective in predictive healthcare and can promote the progress of medical technology to benefit public health. © 2024 IEEE.

关键词： Emotion Recognition

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Machine Learning based Modeling of Drugs using Virtual Screening and in Silico Approach 5

Machine Learning based Modeling of Drugs using Virtual Scree...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Balamurugan, K.S. Appathurai, K. Sathish Kumar, P.J. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Kg College of Arts and Science Department of Computer Technology Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Jeppiaar Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process and implementation of drug development is a complex structure which is a time-consuming process with higher accuracy. A transformative approach to optimize drug discovery is achieved through the integration of drug discovery with a virtual screening process. This is implemented through the in silico machine learning process. The traditional methods involve extracting and testing numerous chemical compounds in vitro and in vivo in the identification of potential drug contenders. These methods lead to various drawbacks that include higher costs with timelines. To overcome the drawbacks of the existing system, the integration of paradigm modeling is initiated. The behavior of molecules with biological targets is determined through In-silico machine learning algorithms. The effective analysis of millions of chemical compounds is done using a virtual screening process through computational simulations and desired pharmacological effects. The machine learning algorithms help in the extraction of intricate patterns from the molecular dataset. Certain properties such as molecule binding affinity, bioavailability, and toxicity are predicted using these algorithms. They help to optimize the molecular structure in silico to improve the interaction with the target proteins. The reliability is achieved through the quality of the training data with the robustness of the algorithm. They help to generalize newer chemical combinations that provide the solution for various diseases with personalized recommendations through the aid of artificial intelligence. © 2024 IEEE.

关键词： Complex structure Computational simulations Drug development Machine learning process Pharmacological effects Virtual screening process

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Optimised Data Encryption and Search Efficiency Framework Using Memory Modules for IoT Applications 2

Optimised Data Encryption and Search Efficiency Framework Us...

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2nd International Conference on Computational Intelligence, communication Technology and Networking, CICTN 2025

作者： Gakhar, Suket Kulkarni, Soham Sunil Kumar, Anant Sharma, Vipin Kumar Arora, Sneha Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Computer Science University of California Irvine Irvine United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331530389

The proliferation of IoT devices necessitates secure and efficient mechanisms for data encryption and retrieval. This paper presents an optimized framework that leverages AES encryption integrated with memory modules to enhance security and operational efficiency. The proposed system introduces a dynamic key generation mechanism to mitigate key reuse vulnerabilities and incorporates a memory module to achieve reductions in encryption and search execution times by 80% and 79%, respectively, compared to traditional methods. The framework also ensures secure cloud storage of encrypted data and supports privacy-preserving retrieval using trapdoor functions. A Java-based implementation validates the system's scalability, robustness, and platform independence for real-world IoT applications. By addressing key challenges such as data integrity, confidentiality, and low-latency operations, the proposed framework offers a comprehensive solution tailored to the evolving needs of the IoT ecosystem. © 2025 IEEE.

关键词： Data integrity

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