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A novel approach for context-aware sensor optimization in a smart home 4th

A novel approach for context-aware sensor optimization in a ...

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4th international conference on Innovative data Communication technologies and Application, ICIDCA 2022

作者： Shrinidhi, L. Kadiresan, Nalinadevi Parameswaran, Latha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Ambient Intelligence is an emerging technology that allows for reasoning of activities that are carried out in a smart environment. Smart sensors play a vital role in activity recognition tasks. Though a great number of sensors provide more pinpointed information about where and when the interaction in the environment occurred, addition of sensors also results in higher power consumption and greater cost constraints. The number of training samples used for activity recognition tasks also increases and becomes computationally expensive. Thus, to overcome these constraints sensor optimization in smart environment is carried out. Mapping of sensors to a user's activity becomes tedious when more than one activity is carried out at the same timestamp. Unambiguous sensor-activity relationship is derived with the help of spatial and temporal context represented by ontology model that captures real time sensor data. Since there exits spatial noise during activity annotation process, it is eliminated with help of pointwise mutual information score. Spatial and temporal data of an activity helps sensor optimization in multi resident living with concurrent activity occurrence. Inactive sensors are removed using mutual information score and redundant sensors are contextually paired based on where an activity happens. By doing so, only unique set of sensors are paired every time even when concurrent activity occurs. The quality of features derived is tested by testing the accuracy of the model by activity recognition task using Random Forest algorithm. Nearly 24% of sensors were identified as inactive in the smart environment. The accuracy of Human Activity Recognition (HAR) task remained constant even after removing the inactive sensors. Addition of spatial context helped to improve HAR with increased accuracy. Contextual pairing of redundant sensors resulted in minimal drop in the model's accuracy. © 2023 The Authors. Published by Elsevier B.V.

关键词： Ontology

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Meeting the Growing Needs in Scientific and Technological Terms with China’s Terminology Management Agency – CNCTST 4th

Meeting the Growing Needs in Scientific and Technological Te...

引用

4th international conference on Human Interaction and Emerging technologies: Future Applications, IHIET – AI 2021

作者： Du, Jiali Alexandris, Christina Pei, Yajun Lian, Yuming Yu, Pingfang Lab of Language Engineering and Computing Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China National University of Athens Athens Greece China National Committee for Terms in Sciences and Technologies Beijing China Beijing International Institute of Urban Development Beijing China

ISBN: (纸本)9783030732707

China National Committee for Terms in sciences and technologies (CNCTST) is a top management agency in China. It was established in 1985, aiming to introduce new terminology, endorse changes of borrowed terminology, alter the standards of terminology, explain unfamiliar terminology, and adopt practice to make terminology more accurate. CNCTST tries to standardize terminology practices in the country. Under the management of CNCTST, a considerable number of terminology databases have been constructed to serve the academic research and practical translation. Furthermore, CNCTST officially launched the first phase of DISE (data intelligent search engine) platform named TERMONLINE on June 15, 2016, and launched the revised second phase of TERMONLINE on September 17, 2020. This article will discuss both different periods of CNCTST and its successful management in Chinese terminology research and practices. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Terminology

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A Survey on Real Time Cloud-based Robotics Systems

A Survey on Real Time Cloud-based Robotics Systems

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Contemporary Information Technology and Mathematics (ICCITM), international conference on

作者： Dhuha Basheer Adbullah Mohanad Rafaa Mohammed Computer Science Department College of Computer Science and Mathematics University of Mosul Mosul IRAQ

Cloud-based robotics has surfaced as a technology that allows robots and cloud services to work together. advances in high-speed Networks, huge data storage, and high computing power have made it possible. Robots benefit from cloud computing because it provides them with more powerful processing capabilities and larger data storage capacities. It also provides open-source software, large databases, and cooperative learning opportunities through information sharing. Recent advancements in cloud robotics have prompted on-going research in this area. In this survey article, we look at the latest developments in cloud-based robotic and its application. We collect bits of information on the most recent developments in cloud-based robotic and examine the problem and constraints in momentum writing, as well as open research topics and future exploration directions.

关键词： Cloud computing Memory Writing Real-time systems Mathematics Research initiatives Information technology

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A Review on DDoS attack Detection in SDN using ML

A Review on DDoS attack Detection in SDN using ML

引用

advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ritu Raj Sandeep Singh Kang University Institute of Engineering Computer Science & Engineering Chandigarh University Mohali India

The world has completely transformed in terms of technology as a result of virtualization. One application of this virtualization is SDN. In recent years, it has become a popular and widely utilised network architecture. Using SDN, or a software-defined network, the control network is divided from the data plane. Although Software Defined Networks provide a simple and obvious method for network control, they have also created a new security danger. DoS attacks, man-in-themiddle attacks, and other threats are examples of these dangers. The most frequent and common danger on this list is DDoS. By impacting the server, DDoS made it simpler to start the attack. This results in the network as a whole failing. Therefore, it is essential to identify this threat in order for a server to operate safely. This research analyses the various machine learning approaches that are employed in the SDN context to identify DDoS attacks.

关键词： Systematics Machine learning Telecommunication traffic Denial-of-service attack Servers Security Reliability

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Music Recommendation Engine Using Emotion Synthesis Through Facial Recognition 1

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5th international conference on Smart computing and Informatics, SCI 2021

作者： Mogshetty, Yogesh Vandure, Sakshata Vivekananda, N. Vandure, Sammed Akiwate, Bahubali Department of Computer Science and Engineering KLE College of Engineering and Technology Karnataka Chikodi India

ISBN: (数字)9789811697050

ISBN: (纸本)9789811697043

Facial expressions are a form of nonverbal communique that is important in facilitating human interactions. It is specifically essential to discover emotions on the face as it has an extensive variety of packages within the discipline of artificial intelligence. Facial image-based mood detection strategies offer a higher technique for noninvasive temper detection and suggesting songs in keeping with the mood. To cope with those problems, the proposed system will automatically suggest the songs according to the user’s mood. The facial image-based mood detection system has many applications including, human behavior understanding, and synthetic human expressions, but not limited to the same. Recognized techniques utilized particularly in literature for the automatic systems are based on look and geometry. Facial image-based tamper detection system generally performed in four-tiers which includes preprocessing of the statistics, face detection, function extraction from the data, and expression class from the facts. Here we have carried out convolution neural network (CNN) to perceive the key human feelings like sadness, anger, fear, happiness, and neutrality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Face recognition

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Optimal Grouping and Belief based CH selection in mobile ad-hoc network using Chunk Reliable Routing Protocol

Optimal Grouping and Belief based CH selection in mobile ad-...

引用

2021 international conference on Advance computing and Innovative technologies in Engineering, ICACITE 2021

作者： Saravanan, T. Thillaiarasu, N. Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Reva University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9781728177410

Transferring ecological data to a mobile ad-hoc network (MANET) is a hard issue. Bunching is one of the advances used to control the transmission of information across intuitive hubs. Each gathering of hubs has at least one chose group head(s), where all bunch heads are associated with one another and are the spine of the information transmission. In addition, cluster leaders can maintain long-Term communication with limited energy sources. Idle terminals and bunch go through checking energy quicker and lessen stage span. For this situation, picking the best bunch scenes with dependable data plays an significant part in the general introduction. To address those issues, this paper proposes an Chunk top path selection (CTPS) using swarm intelligence and hybrid meta-heuristic techniques. The cluster head(s) collection was base on an competent faith replica using Enhanced Monkey inspired optimization (EMIO) algorithm. Then optimal path has found with the help of improved pattern search (IPS) algorithm. Therefore, the purpose of Mobile Ad-Hoc Network (MANET) that uses practical steering procedure is additional trust compare to the hastyone. This method aims to select reliable permanent cluster heads (i.e.) that can provide secure communication through communication nodes. NS-2 simulation demonstrates as to our specific program outperforms obtainable methods in conditions of performance, power consumption, latency, pocket overhead, and pocket delivery speed. © 2021 IEEE.

关键词： Optimization

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Software Reliability: Elements, Approaches and Challenges

Software Reliability: Elements, Approaches and Challenges

引用

advances in computing, Communication & Materials (ICACCM), 2020 international conference on

作者： Garima Rathi Umesh Kumar Tiwari Neelam Singh Department of Computer Science Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9781665474405

Software reliability is the possibility of error-free operation of software over a defined period of time, Time spent in a certain context. Software reliability also has a big impact on system reliability. A key factor in determining the software's quality is reliability. Using data from development processes and statistical techniques, reliability estimation attempts to forecast future reliability values. It is regarded to be a fundamental aspect of software development. a study based on reviews. An assessment of the previously created methods for reliability estimate and prediction is presented in this research. This paper provides a critical analysis of effective investigation into dependability estimation. This paper also poses a number of difficulties. and reliability estimation for the duration of the software instigation process. Further, this paper provides a tenuous overview of earlier studies and identifies contributing elements. which, despite being dominant in the reliability of programs, are still excluded. This work helps designers for understanding the challenges in estimating the reliability of software with the slightest risks.

关键词： Estimation Software quality Error-free operations Reliability engineering Software reliability Reliability

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A Novel Prediction Analysing the False Acceptance Rate and False Rejection Rate using CNN Model to Improve the Accuracy for Iris Recognition System for Biometric Security in Clouds Comparing with Traditional Inception Model

A Novel Prediction Analysing the False Acceptance Rate and F...

引用

advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Noor Basha Shaik Riyaz V. Parthipan Department of Computer Science and Engineering Saveetha University Chennai Tamilnadu India

The main motivation of the study is to improve the Novel Prediction of accuracy using the Convolutional Neural Networks (CNN) model system for iris recognition biometric security in clouds and comparing with Traditional inception models (TIM). Accuracy to perform two groups CNN model and Traditional Inception Models (N=10) to calculate and find the comparison value of accuracy. G power 80% threshold 0.05%, 95% confidence interval mean and standard deviation The independent sample T-test was used Convolutional Neural Networks and TIM. CNN (92%) performs better than TIM (60%). There is a statistically relevant disparity between the CNN and TIM transform based on comparison ratio data is 0.048 (p<0.05). The result shows the proposed CNN algorithm has the better accuracy compared to TIM algorithm.

关键词： Analytical models Biological system modeling Computational modeling Transforms Predictive models Prediction algorithms Convolutional neural networks

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Prediction and Its Impact on Its Attributes While Biasing MachineLearning Training data

Prediction and Its Impact on Its Attributes While Biasing Ma...

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Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： J. Kavitha J.Sasi Kiran Srisailapu D Vara Prasad Krushima Soma G Charles Babu S Sivakumar BVRIT HYDERABAD College of Engineering for Women Hyderabad Telangana Lords Institute of Engineering and Technology Himayath Sagar Hyderabad Telangana GITAM Deemed to be University Hyderabad Telangana Malla Reddy University Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Lakshmi Bangaru Arts and Science College Melmaruvathur India

Machine learning models are built utilizing biased training data that comes from human experience. The data gathered reflects the cognitive bias that human's display in their actions and thought processes. The most effective machine learning models are considered to resemble human cognition; as a result, these models are biased. For improved explainable models, bias detection and evaluation are crucial. This study identifies bias in learning models in relation to cognitive bias in humans and suggests a cutting-edge method for identifying and evaluating machine learning bias. It also tries to identify bias in the dataset. The potentially skewed qualities are seen in the deployed dataset. Prior to employing the notion of alternation function to swap the values of PBAs and analyze the influence on prediction using KL Divergence, we first choose a few common biased characteristics. Compare the KL divergence values from the various models used to train the dataset and forecast the output in this section.

关键词： Training Computational modeling Training data Machine learning Predictive models Prediction algorithms data models

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Generating Higher Order Mutants using PSO with Levy Flight(LFPSO) Algorithm

Generating Higher Order Mutants using PSO with Levy Flight(L...

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Machine Learning, Computer Systems and Security (MLCSS), international conference on

作者： Subhasish Mohanty Jyotirmaya Mishra Sudhir Kumar Mohapatra Sachikanta Dash Sasmita Padhy Sameer Kumar Das Computer Science and Engineering GIET University Gunupur Odisha Faculty of Emerging Technologies Sri Sri University Odisha India School of Computing Science and Engineering VIT Bhopal University Bhopal MP Computer Science and Engineering SOA University Bhubaneswar Odisha

Nowadays, test data generation via a search based become popular, and mutation testing plays a very essential role by improving the efficiency of test data creation. In this research, LFPSO with Mutation Testing, a high-order mutation test data generating method, was presented, which was then implemented on large programs from the Software-artifact Infrastructure Repository (SIR). The suggested algorithm LFPSO is then compared against GA, as well as the most extensively used method for developing test data, CRO. For the majority of the benchmark programs, the results show that LFPSO outperforms GA and CRO in terms of computing efficiency.

关键词： Machine learning algorithms Filtering Machine learning C++ languages Benchmark testing Computer performance C# languages

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