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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Bandna Thakur Allirani P Prashant Chourasiya K Srinu Mohammed Al-Farouni Santhosh S Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Artificial Intelligence & Machine Learning Aditya Engineering College Surampalem India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of ISE Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively.

关键词： Training Schedules Computational modeling Signal processing algorithms Gaussian processes Predictive models Signal processing Data models Telecommunication computing Tuning

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A memetic algorithm based on edge-state learning for max-cut

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Expert Systems with Applications 2022年 209卷

作者： Zeng, Zhi-zhong lü, Zhi-peng Yu, Xin-guo Wu, Qing-hua Wang, Yang Zhou, Zhou National Engineering Research Center for E-Learning Faculty of Artificial Intelligence in Education Central China Normal University Wuhan430079 China School of Computer Science and Technology Huazhong University of Science and Technology 430074 China School of Management Huazhong University of Science and Technology 430074 China School of Management Northwestern Polytechnical University Xi'an710072 China

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning;none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges’ states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a pe

关键词： Population statistics

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Incremental Quaternion Random Neural Networks 7th

Incremental Quaternion Random Neural Networks

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7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022

作者： Cui, Xiaonan Wang, Tianlei Chen, Hao Lei, Baiying Vidal, Pierre-Paul Cao, Jiuwen Machine Learning and I-health International Cooperation Base of Zhejiang Province and Artificial Intelligence Institute Hangzhou Dianzi University Zhejiang Hangzhou310018 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China Université Paris Descartes Paris75270 France

ISBN: (纸本)9789819906161

Quaternion, as a hypercomplex number with three imaginary elements, is effective in characterizing three- and four-dimensional vector signals. Quaternion neural networks with randomly generated quaternions as the hidden node parameters become attractive for the good learning capability and generalization performance. In this paper, a novel incremental quaternion random neural network trained by extreme learning machine (IQ-ELM) is proposed. To fully exploit the second-order Q-properness statistic of quaternion random variables, the augmented quaternion vector is further applied in IQ-ELM (IAQ-ELM) for hypercomplex data learning. The network is constructed by gradually adding the hidden neuron one-by-one, where the output weight is optimized by minimizing the residual error based on the fundamental of the generalized HR calculus (GHR) of quaternion variable function. Experiments on multidimensional chaotic system regression, aircraft trajectory tracking, face and image recognition are conducted to show the effectiveness of IQ-ELM and IAQ-ELM. Comparisons to two popular quaternion RNNs, Schmidt NN (SNN) and random vector functional-link net (RVFL), are also provided to show the feasibility and superiority of using quaternions in RNN for incremental learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image recognition

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Medical Image Classification Using Thermal Images and Diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： Govinda Rajulu. G L. Sharmila D. Venkatesan K Srinivas Nallam Krishnaiah K. Laxman Kumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology. Avadi Chennai Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin's Engineering College Hyderabad India Deptarment of CSE(AI & ML) St. Martin's Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Secunderabad Telangana India Deptartment of Computer Science and Design St. Martin's Engineering College Hyderabad India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Lung and Colon (L&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer; however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of L&C tumors were classified using a hybrid Deep learning (DL) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the LC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known DL models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries

关键词： Technological innovation Accuracy Lungs Subject matter experts Medical services Colon Medical diagnostic imaging Cancer Tumors Testing

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Advancing Epilepsy Care with Neuro-Integrated Predictive Monitoring and Patient Wellbeing Using RNN Model

Advancing Epilepsy Care with Neuro-Integrated Predictive Mon...

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International Conference on Smart Computing & Communications (ICSCC)

作者： A Mahesh K. Sangeethalakshmi A. Nithya M. Shafiya Banu N. Gnanavel M. Muthulekshmi Department of Computer Science and Engineering Sri Sairam College of Engineering Bengaluru Karnataka India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Information Technology St.Joseph's Institute of Technology Chennai Tamil Nadu India Deapartment of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Sciennce and Engineering Vinayaka Mission's Research Foundation(DU) Vinayaka Mission's Kirupananda Variyar Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Naduz India

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

This research presents a novel strategy for improving the treatment of epilepsy by combining neurological monitoring with Internet of Things (IoT) technology. The findings of this study have the potential to significantly advance the field. The methodology that has been developed allows the predictive surveillance of seizure occurrences by the seamless integration of real-time data collected from wearable devices with sophisticated analytics. The system uses algorithms designed to learn from machine data to recognize tiny physiological changes that precede seizures. This enables early intervention. The IoT-enabled platform provides therapeutic insights in addition to seizure prediction. These insights are obtained by combining information supplied by the patient with objective data. This helps to create a full knowledge of both triggers and reactions. This game-changing collaboration between neurology and the Internet of Things gives patients individualized insights, enabling them to proactively manage their illnesses and make choices based on accurate information. Ultimately, this study will alter epilepsy treatment by improving patient wellness, clinical decision-making, and the possibility of developing innovative therapeutic options.

关键词： Technological innovation Neurology Surveillance Epilepsy Medical services Predictive models Prediction algorithms Real-time systems Internet of Things Wearable devices

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Natural Language Processing in Virtual Assistants Current Approaches and Challenges

Natural Language Processing in Virtual Assistants Current Ap...

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Science, technology, engineering and Management (ICSTEM), International Conference on

作者： Gautam Solaimalai J. Maria Shanthi Sampath Kumar S Priyanka Khabiya K. Geetha Gokul Talele US. Bank Software Engineering Manager Georgia United States of America Department of Artificial Intelligence and Machine Learning J B. Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science Engineering Mandsaur University Mandsaur Madhya Pradesh India Department of Computer Science G. T.N Arts College Dindigul Tamil Nadu India Department of Data Science Organization name Accenture Pune Maharashtra India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The proliferation of virtual sidekicks in colorful disciplines has prodded a swell of interest in advancing Natural Language Processing(NLP) ways to enhance their effectiveness. This paper provides a comprehensive review of the current approaches and challenges encountered in integrating NLP into virtual sidekicks. It begins by outlining the foundational generalities of NLP and its vital part in enabling mortal- suchlike relations with virtual sidekicks. latterly, it delves into a discussion of the different methodologies employed in NLP for understanding and generating mortal language, ranging from rule-grounded systems to deep literacy models. likewise, the paper highlights the crucial challenges such as nebulosity resolution, environment understanding, and handling different verbal variations that stymie the flawless functioning of virtual sidekicks. By synthesizing perceptivity from recent exploration trials, this paper offers precious perspectives on the state-of-the-art NLP ways and directions for unborn exploration to overcome the challenges and propel the development of further intelligent and intuitive virtual sidekicks.

关键词： Reviews Virtual assistants Propulsion Natural language processing Distance measurement

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Real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： V. Usha Sandeep Karagatla P. Sheela Gowr P.M.G Jegathambal A. Lizy V. Sathya Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE (AI&DS) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Vels Institute of Science Technology & Advanced Studies Chennai Dept. of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala (R&D) Institute of Science and Technology Chennai India Dept of Information Technology Velammal Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The Internet of Things (IoT) grounded solution of Real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments.

关键词： Animals Tracking Heart beat LoRa Real-time systems Mobile applications Safety Internet of Things Monitoring Global Positioning System

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Analysis Of A Zipline Robotic System To Assist Astronauts On Lunar And Martian Terrains 73

Analysis Of A Zipline Robotic System To Assist Astronauts On...

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73rd International Astronautical Congress, IAC 2022

作者： Kadagadakai, Vishnurat Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Rao, Bhavana B. Pulicallu, Greeshmanth Rai, Riddhi Shukla, Aayush Mitra, Ruhi Nanditha Prabhu, M. Kamath, Satwik Preetham, M.P. Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Communication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

In the realm of drones and probes, this paper proposes a subsequent derivative idea of ziplining automated mobile systems to assist astronauts on Lunar and Martian surfaces. Although the conventional human-like robotic systems alleviate many challenges to astronauts on the moon and the mars, one particular challenge of reaching farther on land is left to the technology of drones. Drones however are more efficient in overviewing the area and are less effective in transporting samples or astronauts to the target. This paper discusses an idea of a ziplining robotic system that can reach farther places from the base camp in a secure and safe way. The system comprises multiple support poles, a subsystem of ziplining and a mobility subsystem to traverse the contents from point to point. This paper describes the overall design of the robotic system, its operations and possible advancements for the future scope. The future applications of such an idea involves the support for constructing a settlement for our Martian outposts. The paper explores these domains of applications and the effectiveness of such a mobility system in various situations. The paper investigates the proposed design on Lunar and Martian surfaces but can be optimised for other planetary bodies as well. © 2022 International Astronautical Federation, IAF. All rights reserved.

关键词： Drones

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Autonomous Vehicles: Steering the Future of Transportation with Self-Driving technology and Advanced AI Systems

Autonomous Vehicles: Steering the Future of Transportation w...

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Global Conference for Advancement in technology (GCAT)

作者： Daniel Nareshkumar. M Desidi Narsimha Reddy A. Sowmiya V. Samuthira Pandi Rayala Sateesh N. Hindumathy Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of EEE Rajalakshmi Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of ECE MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Autonomous vehicles (AVs) are a game-changer in the transportation industry; they will alter cityscapes, cut down on accidents, and reimagine how people experience mobility. This study investigates the complex relationship between autonomous vehicles and high-tech AI systems, looking at how these two technologies can influence transportation in the future. The paper highlights crucial developments in sensor fusion, machine learning algorithms, and real-time data processing that are essential for the development of fully autonomous systems by analyzing the present status of AV technology. In the first part of the study, the basic technologies that support AVs are described. These technologies include LiDAR, radar, cameras, and GPS. When taken together, they allow AVs to perceive their surroundings and locate themselves. After that, it delves into how AI is helping AVs understand sensor data, make judgments in real-time, and carry out complicated maneuvers, showcasing how advancements in deep learning, neural networks, and predictive analytics are powering these capabilities. Integrating AVs into current transportation infrastructures presents both potential and obstacles, which the paper delves into. Critical situation safety, legal frameworks, cybersecurity risks, and AI decision-making ethics are all covered. In addition to the environmental advantages of improved traffic flow and decreased emissions, the study delves into the possible socioeconomic implications, including shifts in urban design, public transit, and the labor market. The research delves into the existing deployment tactics and the path for scaling AV operations through a thorough analysis of case studies, pilot projects, and industry reports. It determines how prepared different groups are to accept this revolutionary technology, including the general public, lawmakers, and manufacturers. To sum up, the study highlights how important it is for academic institutions, business executives, and governm

关键词： Ethics machine learning algorithms Urban planning Decision making Sensor fusion Data processing Real-time systems Safety Computer security Autonomous vehicles

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A Strategic Approach To Tackle Interplanetary Communication Delay: Exploiting artificial intelligence Solutions For Future Space Exploration 73

A Strategic Approach To Tackle Interplanetary Communication ...

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73rd International Astronautical Congress, IAC 2022

作者： Rai, Riddhi Thuluva, Sushmith Kodukula, Ananya Bharadwaj, Vyoma Jain, Alankriti Shadaksharaiah, Anushree Maligehalli Pulicallu, Greeshmanth Kadagadakai, Vishnurat Shukla, Aayush Mitra, Ruhi Nanditha Prabhu, M. Preetham, M.P. Department of Artificial Intelligence & Data Science Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Computer Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Telecommunication Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Electronics & Instrumentation Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Mechanical Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Artificial Intelligence & Machine Learning Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India Department of Information Science & Engineering Ramaiah Institute of Technology MSRIT Post M S Ramaiah Nagar MSR Nagar Karnataka Bengaluru560054 India

Despite the extreme measures in any interplanetary mission, a probe is inevitably prone to a handful of unexpected errors. Concerning this, an exceptional challenge in every interplanetary endeavour is the inescapable communication gap (delay) resulting from the various factors. Although the gap is in the range of few minutes to seconds, the actions performed by the probe during critical events creates the necessary to establish a continuous and real time communication with the mission control. Thinking long term on manned missions to Mars and beyond, escalates the concern *** currently tackling such communication issues faced when a satellite communicates with earth is of high *** current paper proposes an alternate strategy to tackle this subject. With recent advancements in the development of artificial intelligence (AI), state-of-the art algorithms,neural networks, and reinforcement based models, it can be so optimized for applications suitable to meet the needs of satellite communication issues. In the light of which, the paper investigates the design of a monitoring satellite. This monitoring satellite is trained with the data and a developed deep learning algorithm/machine learning model is employed by the satellite to make future decisions. Hence using the current knowledge and data we have regarding the communication issues faced while establishing a link with earth, we can train the data models present in the satellite to make the communication with earth more *** the results we gain from this can be later transferred to tackle interplanetary communication *** in the future,we can transfer this knowledge to a a probe that can help tackle interplanetary communication. Copyright © 2022 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Satellites

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