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A Deep CNN self-Attention Model for Multidimensional speech Quality Prediction Using Crowdsourced Datasets

A Deep CNN Self-Attention Model for Multidimensional Speech ...

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Advances in Electrical, Computing, Communication and sustainable Technologies (ICAECT), International Conference on

作者： Annapurna Gummadi Chamandeep Kaur Deepak Gupta Vuda sreenivasa Rao santosh Tripurana P. Nagaraj Department of CSE (Data Science) CVR College of Engineering Hyderabad India Computer Science & Information Technology Department Jazan University Jazan Kingdom of Saudi Arabia Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamil nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios

关键词： Measurement Adaptation models Convolution Computational modeling Transfer learning Interference Predictive models User experience Data models Convolutional neural networks

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Fuzzy-Based Cluster Head Management with Artificial Flora Optimization for Energy-Efficient and secure Routing in Fog-Enabled Wireless sensor Networks

Fuzzy-Based Cluster Head Management with Artificial Flora Op...

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Innovations in High speed Communication and signal Processing (IHCsP), International Conference on

作者： Deepak Dasaratha Rao Vandana Roy Kiran Bhopte K. Mukilan Prashant Kumar shukla Rajendra V. Patil Texas USA DoEC GGITS Jabalpur M.P. India IES Institute of Pharmacy IES University Bhopal Madhya Pradesh India Dept. of Civil Engineering Kalasalingam Academy of Research and Education TN India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Engineering SSVPS Bapusaheb Shivajirao Deore College of Engineering Dhule M.S. India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

A wide variety of smart home, smart city, and smart space automated applications have contributed to the Internet of Things' rapid growth in the past few years. A lot of real-time Internet of Things (IoT) applications rely on Wireless sensor Networks (WsN) because of how reliable, efficient, flexible, and scalable they are. It has a cluster of geographically specific sensors that capture and analyze data about the physical characteristics of the surrounding area. Increased latency and decreased performance are consequences of the massive amounts of information being handled and saved in the cloud. Particularly in mission-critical, delay-sensitive Internet of Things applications, concerns like latency, protection, and conservation of energy must be addressed. To get around this problem, fog technology has been implemented, whereby end-user-located IoT devices do all the processing and storage locally, rather than constantly sending raw data to the internet. Fog computing in conjunction with WsN provides excellent options for monitoring surroundings pertaining to monitoring with their inexpensive, environmentally friendly, and low-bandwidth sensors, as well as for developing a safe and efficient framework for data transfer. The primary goals of this study are to design a deep learning-based intrusion detection mechanism for use in fog-based WsN environments, as well as an energy-efficient cluster-headed organization and multi-objective optimum routing method. The findings reveal that the recommended FAFOC system outperforms LEACH, P-sEP, and FEAR in network lifespan, active nodes, and usage of energy, 98% of packet distribution ratio, and latency. FAFOC is the most cost-effective network of wireless sensors because fuzzy reasoning and artificial vegetation optimization boost network effectiveness.

关键词： Wireless communication Wireless sensor networks Cloud computing Flora Routing Data transfer Energy efficiency Internet of Things Optimization Monitoring

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Machine Learning based Cybersecurity Technique for Detection of Upcoming Cyber Attacks

Machine Learning based Cybersecurity Technique for Detection...

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International Conference on Communication and Electronics systems (ICCEs)

作者： Kanchi Lohitha Lakshmi s.shobana Bathula Prasanna Kumar K.B.Glory Bonda Kiran Kumar sandya Rani Department of Computer Science and Engineering Vasireddy Venkatadri Institute Of Technology Guntur Department of IT R.M.K Engineering College Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Guntur AP Department of Engineering English Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh State India Department of Architecture KL School of Architecture KL Deemed to be University Vaddeswaram Dept. of IT St. Martin's Engineering College Secunderabad Telangana India

Cyberattacks are prevalent in the age of the Internet. Each year, both the quantity and severity of cybercrimes increase. Protection against cyber-attacks has become a primary responsibility, significant in the internet society of today. However, providing cyber security is a highly difficult task that requires experience in the field of attacks and the ability to evaluate the possibility of threats. The continual evolution of cyberattacks is the biggest challenge in this industry. This article describes the significance of cyber security and enumerates the hazards that exist in the present digital environment. The statistics and evaluation of cyberattacks demonstrate the seriousness of these occurrences. several sorts of cyber security threats are outlined, along with the machine learning approaches that may be used to detect these attacks.

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An Analysis of Lunar Landing Pad Designs and Construction Methods 6

An Analysis of Lunar Landing Pad Designs and Construction Me...

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6th International Conference on Additive Manufacturing, ICAM 2021

作者： Campbell, Andres I. Carson, Helen C. De soto, Miriam Fiske, Michael R. Martin, Luke E. Murai, Vincent R.T. Ramirez, Fernanda s. Romo, Ethan M. schang, Kayla E. smith, Kaveon C. Dept. of Integrated Engineering Minnesota State University 227 Wissink Hall MankatoMN56001 United States Dept. of Materials Science and Engineering University of Washington 302 Roberts Hall Box 352120 SeattleWA98195 United States Engineering and Computer Science Dept. California State University Fullerton Fullerton 800 N. State College Blvd. FullertonCA92802 United States Advanced Materials and Manufacturing NASA/MSFC Space Technology Development Branch Jacobs Space Exploration Group Martin Rd. SW HuntsvilleAL35806 United States Dept. of Engineering Management Arizona State University 699 S Mill Ave. TempeAZ85281 United States Engineering Dept. University of Hawaii at Manoa 2540 Dole St. HonoluluHI96822 United States Engineering Dept. Santa Ana College 1530 W 17th St. Santa AnaCA92707 United States Physics Dept. University of California Santa Cruz 1156 High St. Santa CruzCA95064 United States Dept. of Physics University of Central Florida 4000 Central Florida Blvd. OrlandoFL32816 United States Dept. of Computer Science Oklahoma State University 218 MSCS Bldg. StillwaterOK74078 United States

ISBN: (纸本)9780803177352

since the Apollo era, the debris-generating interactions between a rocket and extraterrestrial surfaces have caused known risks to large-scale lunar and planetary exploration. In situ resource utilization (IsRU) technology has the potential to solve this challenge by enabling the construction of 3D-printed landing and launch pads. This paper discusses past efforts and designs of these pads with a focus on material sintering and extrusion methods of additive construction. Proposals that are explored include tiles-based construction techniques, lunar regolith sintering methods, 3D architectures to control dust and exhaust plumes, and aerodynamically designed exterior structures. Developments in additive construction methods and materials are also discussed in the context of IsRU landing pad compatibility and potential design constraints. This paper analyzes the strengths and weaknesses of 11 concepts, providing insights into the design, construction, testing, and analysis of landing pad designs that are essential to realizing human habitation on the Moon. Copyright © 2022 by AsTM International.

关键词： sintering

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Novel Ultrasonic Image Reconstruction and Deep Learning Model-Based Categorization for Accurate Liver Disease Diagnosis

Novel Ultrasonic Image Reconstruction and Deep Learning Mode...

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Data science and Network security (ICDsNs), International Conference on

作者： Nijaguna G s sneha sureddy BinduMadhavi Dayanidhi Mohapatra Parameshachari B D Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Department of ECE Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The diagnosis of liver illnesses is critical for efficient treatment and management of the condition. By combining deep learning model-based classification with ultrasonic image reconstruction, the developed method aims to surpass standard deep learning techniques. This method's nine primary processes include pre-processing approaches, image reconstruction, liver area segmentation, feature extraction, deep learning model training, testing, and comparing model performance. The suggested approach was tested on patients with fatty liver disease by processing huge sets of ultrasound pictures and RF data frames. The InceptionResNetV2 CNN was used to detect fatty liver disease in these persons. The proposed approach has a sensitivity of 95.2 percent, an accuracy of 92.8 percent, and a specificity of 94.1 percent. By combining deep learning model-based classification with ultrasonic image reconstruction, the technology overcomes some of the limitations of classic deep learning methods. Medical ultrasound imaging is commonly used because it provides for the non-invasive, risk-free identification of liver disorders. Ultrasonic imaging is a sort of medical image diagnostic that assists doctors in diagnosing and treating a variety of ailments. This suggests that the suggested technique might considerably improve the precision and consistency with which liver disease is detected, resulting in better patient outcomes.

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A Novel advanced Algorithm in Automation of Food and Health Monitoring system using IOT

A Novel Advanced Algorithm in Automation of Food and Health ...

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International Conference for Convergence of Technology (I2CT)

作者： Pattem. sampath Kumar J. Vijayasree K saikumar Mohammed Ayad Alkhafaji Ahmed J. Obaid sajad Ali Zearah Malla Reddy Institute of Engineering & Technology Hyderabad India Department of Electronic and Communication Vignan’s Institute of Information Technology (A) Dept of ECE Koneru Lakshmaiah Education Foundation HyderabadR.V.S Nagar Moinabad-Chilkur Rd near AP Police Academy Aziznagar Telangana India Department of Medical Device Industry Engineering College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Faculty of Computer Science and Mathematics University of Kufa Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq

Food is one of the essential factors in human growth and development. The recent results of the internet of things and robots have already shown the beginnings of a new food monitorization. Automation in the restaurants and food sector did not originate technically, so humanoid-related IoT platforms are necessary. In this research, an advanced monitoring algorithm for the automation of food & health system performs using the (IoT). The suggested system develop using food quality sensing devices. Each detector was equipped with the above qualities and connected using breadboards and relay boards. There is tremendous fear and motivation in the thought that there may not be enough food to go around as the human population expands exponentially. To construct a monitoring system that can identify the presence of people, gases, and temperature differences, we employed website creation in combination with the (IoT). The designed application has attained a sensitivity of 97.53%, an accuracy of 98.83%, a recall of 97.98%, and a detection rate of 98.91%. The implemented technology is beneficial to researchers and society. The proposed IoT-based automation of food & health monitoring system compete with present technology and outperforms the methodology.

关键词： Temperature sensors Automation sensitivity software algorithms sociology sensor phenomena and characterization sensor systems

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In-Heal: Intelligent Healthcare Architecture using AI-based Priority scheduling Mechanism in vsDN-driven Edge Network 7

In-Heal: Intelligent Healthcare Architecture using AI-based ...

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Basu, Deborsi Parui, sricheta Choudhury, Tanvi Ghosh, Uttam G.S. Sanyal School of Telecommunications Indian Institute of Technology Kharagpur India Advanced Technology Development Centre Indian Institute of Technology Kharagpur India Bengal Institute of Technology Management Dept. of ECE West Bengal Santiniketan India Vanderbilt University Dept. of Electrical Engineering and Computer Science NashvilleTN United States

ISBN: (纸本)9781665428491

The advancements of Edge Computing, Artificial Intelligence, vsDN (virtualized software Defined Networking) are significantly boosting up the upcoming smart healthcare systems, but the advanced medical facilities are severely affected by the lack of proper systematic architectures. The chronological up-gradation of the latest technology is not amalgamated according to the existing infrastructure and that is why the fatal cases of medical negligence can not be overlooked, specially during any pandemic or epidemic situation. In this work, we have proposed a novel AI-driven network architecture that will proficiently improve the supervision systems inside smart medical care units. vsDN-driven edge servers are responsible for continuous monitoring of patient inflow and outflow inside any hospital and simultaneously provide the health status of the patients using an AI-based Risk Factor (RF) scale. An intelligent priority scheduling mechanism is designed to selective filter the health conditions and identify the critical patients with immediate medical assistance. Our analysis shows that the proposed method provides better patient-to-doctor mapping using various available media and significantly reduce the cases of medical negligence inside any medical care facility. This dynamic architecture can be easily up-gradable even for the multi-vendor scenario. Our approach is also efficient enough to control any sudden surge of cases that the entire world has recently witnessed due to COVID-19. We have used random real-time patient flow data to validate our claims. © 2021 IEEE.

关键词： Health risks

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Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Evaluation of Deep Training and Adapting Algorithms in Medic...

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

作者： Ali Hadi Abdulwahid Girija Rani Karetla KVB. Ganesh A suresh Kumar K. Radha K. shivakumar Control and Automation Engineering Department Engineering Technical College Southern Technical University Basra Iraq School of Computer Data and Mathematical Sciences Western Sydney University Australia KL Business School Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of CSE Graphic Era Deemed to be University Dept. of IT St. Martin's Engineering College Secunderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

An electroencephalogram that was captured with electrodes placed can easily get contaminated with a variety of artifacts. Here is a comparative of various electroencephalogram (EEG) de-noising techniques. Three alternative techniques are used to eliminate five different kinds of noise, and also the outcomes are evaluated. These three methods are just the Least Mean square (LMs) technique, the Connected Neural Network, and also the Recursive Least - square (RLs) adaptable algorithm (FCNN). Period charts of the actual EEG signal, the noise EEG signal, and thus the predicted EEG signal are used to display the results. RRMsE and signal- to-noise ratio were utilized to compare the effectiveness of the 3 de-noising strategies in this study. Investigating the model parameters in this case, we discover the FCNN forecasts a better outcome compared to the other two methods.

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0. 4V, Low Power GmC Integrator and Bandpass Filter Design for EMG Bio Medical signal Processing 2

0. 4V, Low Power GmC Integrator and Bandpass Filter Design f...

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2nd International Conference on Power and Embedded Drive Control, ICPEDC 2019

作者： Javvaji, Veerayya Musala, sarada Vignan's Foundation for Science and Technology Dept. of Electronics Communication Engineering VadlamudiA.P. India

ISBN: (纸本)9781728124148

This paper presents a novel 0.4 V $G-{m}\mathrm{C}$ integrator design approach and fourth order bandpass filter design for EMG biomedical applications with high DC gain and and low power consumption. The filter and integrator are implemented with a new architecture in 180nm CMOs process. By using negative resistance in proposed integrator the response can be controllable. The negative resistance in this paper is implemented with Operational Trans Conductance Amplifier(OTA) The response of proposed integrator is close to ideal integrator *** OTA used in this work is operated with 0.4 V supply and bulk driven structure. The maximum power consumption of integrator is 45 nW and cut off frequency is 160 Hz. The fourth order bandpass filter is consumes 117nW power with bandwidth 260 Hz. The dynamic range of the filter is 63dB. © 2019 IEEE.

关键词： Operational amplifiers

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A secure and Trustworthy Biometric Data Ecosystem for Cross-border suspect Identification

A Secure and Trustworthy Biometric Data Ecosystem for Cross-...

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IEEE International Conference on Big Data

作者： Katerina Kyriakou Apostolos Apostolaras Polychronis Velentzas Georgios Benos Konstantinos Koutsoukos Chrysostomos symvoulidis Kaitai Liang Zeshun shi Asterios Leonidis Kyriaki Miniadou Eleni Veroni spyridon Evangelatos Georgios Th. Papadopoulos Thanasis Korakis Department of Electrical and Computer Engineering University of Thessaly Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft The Netherlands Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Crete Greece Research & Innovation Development Netcompany-Intrasoft S.A. Luxembourg Luxembourg Dept. of Electronic Engineering Hellenic Mediterranean University Crete Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces the Biometrics Data space framework, which is a secure ecosystem built on Data spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. specifically, it utilizes advanced PETs, including Large-scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

关键词： Biometrics Law enforcement space technology Terrorism Ecosystems Regulation Blockchains Resource management spectrogram Indexing

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