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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Harnit Saini Saroj Bala S.Jhansi Ida K. Suresh Kumar Shankerpally Swetha Divyabarathi P Department of CSE Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department of CSE Kongu Engineering College Erode Department of Information Technology CVR College of Engineering Telangana Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection system (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated.

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A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network

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Measurement: Sensors 2023年 27卷

作者： Ravishankar, T. Nadana Makarand Jadhav, Hemlata Satheesh Kumar, N. Ambala, Srinivas Pillai N, Muthuvairavan Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology KTR Tamil Nadu Chennai India Department of E & T C Marathwada Mitra Mandal's College of Engineering Pune India Department of Electronics and Communication Engineering PBR Visvodaya Institute of Technology and Science Kavali Andhra Pradesh 524201 India Computer Engineering Department Pimpri Chinchwad College of Engineering Sector -26 Pradhikaran Nigdi Maharashtra Pune411 044 India Department of Computer Science and Business Systems R.M.D Engineering College Kavarapettai India

Most women generally have an ovarian cyst, causing the disorder. Pregnant cysts occur when many water-packed tumors appear in the womb. This is especially true for women who have a worthy cause for childbearing. Women are related to menstrual problems and cyst problems during pregnancy. Recently, ultrasound imaging and machine learning techniques have been used to detect ovarian cysts. Different domain experts provide their own decisions on finding out what kind of ovarian cyst it is from ultrasound images. However, a most accurate and uniform decision-making system is necessary for the early detection of cysts. To aid physicians, an automated detection system has been proposed in this paper to make it more effective for physicians to eliminate these problems. This system uses the extracted features from the image for cysts detection and classification. Automatic ovary cyst detection (OCD) and classification are implemented in this work using a fuzzy rule-based Convolutional Neural Network (FCNN). The proposed system (OCD-FCNN) has yielded 98.37% accurate results when tested with benchmark datasets. © 2023 The Authors

关键词： Fuzzy inference

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Efficient Drinking Water Quality Analysis using Machine Learning Model with Hyper-Parameter Tuning

Efficient Drinking Water Quality Analysis using Machine Lear...

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International Conference on ent Computing and Control systems (ICICCS)

作者： M Uma Maheswari R. Sudharsanan M Arthy Anne Jenefer L Oormila V Samuthira Pandi Department of Electrical and Electronics Engineering RMD Engineering College Tamil Nadu India Department of Information Technology SRM Institute of Science and Technology Ramapuram Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Computer Science Engineering Agni College of Technology Tamil Nadu India Department of Electronics ad Communication Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu India

The conventional approaches to determining the quality of water include costly and lengthy statistical and laboratory testing; hence, the concept of real-time monitoring is no longer applicable in the modern day. The disastrous implications of poor water quality demand that there be a remedy that is both more expedient and more realistic. This research study examines a variety of supervised machine learning algorithms in order to identify the quality of the water. A variety of variables, such as the pH value, the hardness, the solids, the chloramines, the sulfates, the conductivity, the organic carbon, the trihalomethanes, the turbidity, and the potability of the water, are essential for determining the quality of the water. Random Forest (RF) and Decision Tree (DT) are used in order to ascertain the caliber of the water that is suitable for human consumption. The standard laboratory approach for testing the quality of water is one that takes a lot of time and may be somewhat expensive at times. In a very short length of time, the algorithms that are proposed in this study are able to provide an estimation of the quality of drinking water. DT has a height accuracy F1 score of 99%, whereas RF has a score of 87.86% and a precision of 82.36%. The difference in these two scores is due to the fact that DT’s precision is lower. The suggested method demonstrates its potential for usage in real-time water quality monitoring systems by achieving appropriate accuracy with a minimal set of parameters. To prove the usefulness of this program, this is essential.

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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

arXiv

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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Analysis of EMI Mitigation Techniques in High Speed Printed Circuit Board

Analysis of EMI Mitigation Techniques in High Speed Printed ...

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Innovation in technology (INOCON), IEEE International Conference for

作者： M. Anandan L. P. Sajitha S. Geerthik Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Information Technology Agni College of technology Chennai Department of Computer Science and Business System R.M.K Engineering College Chennai

To serve the numerous services, today’s wireless communication devices require microwave network systems with multiband and multifunctional capabilities. A growing number of microwave circuits and parts that can handle several frequency bands and larger bandwidths are needed to support the unprecedented systems. The issues in EMI/EMC have grown as a result of research and advances in contemporary wireless network communication equipment. Generally speaking, there are several ways to reduce different EMI issues. Therefore, the necessary tunable and downsized components are required to make these systems smaller, lighter, and less expensive. Most studies on emission measurements have been done in the time domain recently and one of the widely used techniques is the creation of EMI band pass filters. This article presents band pass filters with HIDDS (Hybrid Inter Digital Defected Ground Structure). Additionally, a number of uses for the hybrid S-parameters are provided by fusing Agilent ADS circuit tool and full-wave EM solvers. To provide a complete EM response of the filter construction, 3D EM simulations are performed. The analysis of the simulated findings with the results of previous experiments has revealed that HIDDS (Hybrid Inter Digital Defected ground Structure) provides minimal insertion loss and a larger range of stop band frequencies. Therefore, wireless systems might benefit from the band pass filters with HIDDS that have been proposed.

关键词： Band-pass filters Magnetic separation Wireless networks Electromagnetic interference Insertion loss Microwave communication Microwave theory and techniques

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CancerAI: A Deep Learning Framework for Ovarian Cancer Prediction

CancerAI: A Deep Learning Framework for Ovarian Cancer Predi...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： G. Revathy P. Uma Maheswari S. Madhavi Vijay Anand P R. Vajubunnisa Begum H. Jasmin Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer science and Business systems K. Ramakrishnan college of Engineering Trichy India Department of Information Technology Vel tech Multi tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Electronics and Communication Science Justice Basheer Ahmed Sayeed College for Women Affiliated by University of Madras Chennai India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Ovarian cancer is the fifth most common cause of death from cancer in women. This is mainly because it is often diagnosed at a late stage, as the earliest symptoms are unclear and inconsistent. Existing diagnostic techniques, such as biomarkers, biopsies, and imaging tests, have notable drawbacks such as reliance on subjective interpretation, inconsistency across different observers, and time-consuming testing procedures. This research study introduces an innovative deep learning framework that employs a convolutional neural network (CNN) algorithm to accurately predict and diagnose ovarian cancer, thereby overcoming the existing constraints. The CNN was trained using a dataset of histopathology images. The dataset was partitioned into test and training subsets to enhance the model's performance. The algorithm demonstrated an impressive accuracy rate of 96%, successfully detecting 97.12% of malignant instances and precisely categorizing 95.02% of healthy cells. This method effectively mitigates the challenges related to human expert evaluation, including elevated rates of misclassification and variability across different observers, while also reducing the time required for analysis. The results underscore the capability of this CNN-based technique to offer a more precise, effective, and dependable strategy for forecasting and detecting ovarian cancer. Subsequent investigations will prioritize the integration of new breakthroughs in deep learning to further amplify the efficacy of the suggested approach.

关键词： Deep learning Training Accuracy Histopathology Transfer learning Observers Prediction algorithms Partitioning algorithms Convolutional neural networks Ovarian cancer

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan

Heart Diseases Prediction Using Machine Learning Techniques:...

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Information technology (ACIT)

作者： Raed Alazaidah Mohammed Abu Safaqah Haneen Alzoubi Hani Omar Maram Y. Al-Safarini Esraa Abu Elsoud Ali Elrashidi Mo’Ath Alluwaici Suhaila Abu Owaida Department of Data Science and AI Faculty of IT Zarqa University Zarqa Jordan Department of Mechanical Engineering Global College of Engineering and Technology (GCET) Muscat Sultanate of Oman Department of Computer Science Faculty of IT Applied Science Private University Amman Jordan Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of Information Technology Al-al-Bayt University Mafraq Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan.

关键词： Heart Measurement Machine learning Predictive models Information technology Diseases

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Cloud-Based IoT Solutions for Smart Grids: Advancing Smart Technologies in Energy Management

Cloud-Based IoT Solutions for Smart Grids: Advancing Smart T...

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International Conference on Electrical Power and Energy systems (ICEPES)

作者： Franciskus Antonius Alijoyo Deepak A. Vidhate Chamandeep Kaur V Nivedan V. Kanpur Rani A. Balakumar School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra India Department of Computer Science Jazan University Jazan Saudi Arabia Department of Electronics and Communication System Sri Ramakrishna College of Arts & Science Coimbatore India Hindustan Institute of Technology and Science Chennai India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future.

关键词： Cloud computing Scalability Transforms Real-time systems Smart grids Internet of Things Servers

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Stochastic cluster head selection model for energy balancing in IoT enabled heterogeneous WSN

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Measurement: Sensors 2024年 35卷

作者： Anto Pravin, R. Murugan, K. Thiripurasundari, C. Ranjith Christodoss, Prasanna Puviarasi, R. Abdul Lathif, Syed Ismail Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering KSK College of Engineering and Technology Tamil Nadu India Department of Computing Mathematics and Physics Messiah University Mechanicsburg PA United States Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu India

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness. © 2024 The Authors

关键词： Internet of things

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