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International Conference on ICT in business Industry & Government (ICTbIG)

作者： vandana Roy Lipika Roy Ritu Ahluwalia Geetanjli Khambra M. Ramesh K. Rajasekhar Dept. of EC Gyan Ganga Institute of Technology and Sciences Jabalpur M.P. India UG Course Gyan Ganga Institute of Technology and Sciences Jabalpur M.P. India Department of Computer Applications The Bhopal School of Social Sciences Bhopal M.P India Dept. of IT St. Martin's Engineering College Secunderabad Telangana Bharat Dept. of ECE B V C Engineering College Odalarevu AP Bharat

Cancer has now become a widespread issue, and it might be difficult to diagnose it. The analysis and diagnosis of disease in a patient through a monitoring system has advanced significantly, but there are still some obstacles to overcome. Technology-assisted disease discovery is urgently required since it could enable clinicians to make an accurate diagnosis more quickly. The process makes use of event-driven datasets produced by the Internet of Things (IoT). Rarely are the cloud and IoT used to process the real-time dataset for illness discovery. Cancer detection is a serious difficulty since the data generated by sensors constitutes huge data and cannot be quickly processed without the use of data mining and machine learning. A precise and intelligent method is needed for cancer prognosis because it has been discovered that cancer cannot be accurately predicted from offline datasets. The International Federation of Obstetrics and Gynecology (IFOG) realworld data set has been used to predict cervical cancer using machine learning. Five metrics, including true-positive-rate, false-positiverate, f-measure, MCC, and accuracy, have been used to examine the results of the training of six machine learning models using the latest UCI repository. The outcomes demonstrate that the tree-based decision stump machine learning classifier and the meta-based iterative classifier optimizer achieved the maximum prediction accuracy of 77.97%, which is the greatest among the other ML classifiers. The proposed model demonstrated noteworthy performance in terms of prediction accuracy.

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Audio Forgery Detection Techniques: Present and Past Review 4

Audio Forgery Detection Techniques: Present and Past Review

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4th International Conference on Trends in Electronics and Informatics, ICOEI 2020

作者： bevinamarad, Prabhu R. shirldonkar, M.s. BLDEA'S V.P. Dr. P.G. Halakatti College of Engineering and Technology Affiliated to Visvesvaraya Technological University Department of Computer Science and Engineering Vijayapur Karnataka586103 India

ISBN: (纸本)9781728155180

The increase in low cost digital audio recorders, mobile applications and accelerating growth of the audio-based Internet of Things (IoT) has initiated ease in obtaining speech and audio data which will be later used for the purpose of identifying human traits, implementing voice authentication system and development of voice based embedded systems. On the other hand, the availability of free advanced mobile applications and audio editing software like Adobe, Audition CC, etc. enabling people to edit easily the meaningful content of audio recordings data for getting benefit from e-services or producing it in a courtroom for the purpose digital proof. Perhaps, most of the people do it for fun as well as the strong intention of hiding reality present. Moreover, the audio recording captured in a real-life today does not contain digital watermarking and signature content for authentication because of expensive procedure. Therefore, in recent years the researches focused more on developing active audio forgery detection techniques for copy-move and audio splicing forgeries to authenticate and verify for its genuineness. In this paper, put an effort to describe past and present developments in audio copy-move and audio splicing forgery techniques. The paper also presents an overview of audio forgeries, its classification, various post-processing operations used and available audio dataset for forgery sample preparation for benchmark testing. © 2020 IEEE.

关键词： Classification (of information)

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Improving the prediction accuracy of cyanobacterial blooms using a hybrid model integrating dimensionality reduction and deep learning

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Water biology and security 2025年

作者： Chen, Jiwei Hou, Zhongbao Li, Yunxi Li, Jiexian Li, Lin School of Naval Architecture and Ocean Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Public Health Fu Jen Catholic University Taiwan 242062 China Khoury College of Computer Sciences Northeastern University Vancouver V6B1Z3 Canada Institute of Hydrobiology Chinese Academy of Sciences Wuhan 430072 China

Cyanobacteria Harmful blooms have negatively impacted human socio-economic activities and the natural ecological environment. Predicting cyanobacterial blooms requires the application of advanced analytical models powered by artificial intelligence to accurately model bloom development. In this study, we developed a hybrid model that integrates the Temporal Convolution Network (TCN) with the Long short-Term Memory Network (LsTM) to achieve highly accurate predictions of cyanobacterial bloom events. We used Principal Component Analysis (PCA) during the preprocessing phase to reduce data dimensionality of model input variables and enhance computational efficiency. We used a data set from the Darling River in Australia to conduct a comparative analysis with traditional deep learning models and comprehensively assess the proposed model performance. We also investigated the application of the correlation coefficient matrix method in variable selection. The results demonstrate that the proposed model outperforms conventional methods in predicting cyanobacterial blooms. When benchmarked against the Recurrent Neural Network (RNN) model, our model demonstrates a superior ability to capture the intricacies of time series data. specifically, it achieves a 79.5 % reduction in Mean square Error (MsE) and a 197 % increase in the Coefficient of Determination (R<sup>2sup>). Furthermore, comparative experiments were carried out across several prediction time steps, indicating that the model achieves optimal performance in minimizing both the MsE and Mean Absolute Error (MAE) when predicting one day ahead. Notably, the error distribution is most condensed at this time step, outperforming models with prediction horizons ranging from two to seven days. We also undertook a thorough analysis of the significance of each principal component in model predictions. The results highlight that principal component 5 and principal component 2 exhibit the most substantial contribution to the p

关键词： Cyanobacteria bloom Deep learning Long short-term memory network Prediction Principal component analysis Temporal convolution network

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Implementation of automated framework in healthcare problems throughan intellectual machine learning approach

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AIP Conference Proceedings 2024年第1期2816卷 080006-080006页

作者： Kapula, Prabhakara Rao Rajalakshmi, R. Rajagopal, balaji Ramkumar Kulkarni, Gopal Ramchandra Jaiswal, sushma singh, Charanjeet Department of ECE B v Raju Institute of Technology Telangana Narsapur India Panimalar Engineering College. Chennai ECE Anna University Tamilnadu Chennai India Dept of Artificial Intelligence and Analytics IT Industry Tamilnadu Chennai India Electrical Engineering Annasaheb Dange College of Engineering and Technology MP Ashta 416301 India Department of Computer Science & Information Technology (CSIT) West Bengal India Guru Ghasidas Vishwavidyalaya (A Central University) Koni (C.G.) Bilaspur 495009 India Electronics and Communication Department Deenbandhu Chhotu Ram University of Science and Technology Murthal Hartana Sonipat India

The automated learning models method is highly significant for various health - related issues since this sort of machine learning's analytical approach can particularly detect various difficulties in addition to provide a stronger remedy. In this specific report, a fundamental idea of the automated structure of the model of machine learning has already been provided with appropriate information and data that really can directly and constructively impact various healthcare problems. Each of the methodologies employed by various health-care providers is also mentioned. In addition to something like this, the researcher calmly developed a model which could be utilised to obtain accurate result in the findings section. The researcher also described every one of these outcomes to assist readers grasp the significance of automatic ML. © 2024 Author(s).

关键词： automated healthcare Machine Learning (ML) problem technology

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smart Agriculture and smart Farming using IoT Technology 1

Smart Agriculture and Smart Farming using IoT Technology

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1st International Conference on Applied Mathematics, Modeling and simulation in engineering, AMsE 2021

作者： Gowda, Dankan v. Prabhu, sandeep M. Ramesha, M. Kudari, Jayashree M. samal, Ansuman Department of Electronics and Communication Engineering B.M.S Institute of Technology and Management Karnataka Bangalore India Department of Electronics and Communication Engineering Canara Engineering College Bantwal Mangalore Karnataka Benjanapadavu India Karnataka Bangalore India Department of Computer Science and Information Technology JAIN Deemed to be University Karnataka Bangalore India Faculty of Hospitality and Tourism Management Siksha O Anusandhan Deemed to be University Odisha Bhubaneshwar India

It has become easier to access agriculture data in recent years as a result of a decline in digital breaches between agricultural producers and IoT technologies. These future technologies can be used to boost productivity by cultivating food more sustainably while also preserving the environment, thanks to improved water use and input and treatment optimization. The Internet of Things (IoT) enables the production of agricultural process-supporting systems. Referred to as remote monitoring systems, decision support tools, automated irrigation systems, frost protection systems, and fertilisation systems, respectively. Farmers and researchers must be provided with a detailed understanding of IoT applications in agriculture as a result of the knowledge described above. This study is about using Internet of Things (IoT) technologies and techniques to enhance agriculture. This article is meant to serve as an introduction to IoT-based applications in agriculture by identifying need for such tools and explaining how they support agriculture. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Internet of things

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Rate splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO satellite Networks

arXiv

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arXiv 2022年

作者： Khan, Wali Ullah Ali, Zain Lagunas, Eva Mahmood, Asad Asif, Muhammad Ihsan, Asim Chatzinotas, symeon Ottersten, björn Dobre, Octavia A. University of Luxembourg Luxembourg City1855 Luxembourg Department of Electrical and Computer Engineering University of California Santa Cruz United States Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen Univerisyt Guangdong Shenzhen China The School of Computer Science and Electronic Engineering Bangor University BangorLL57 1UT United Kingdom The Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

Low Earth Orbit (LEO) satellite communication (satCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO satCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RsMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO satCom using RsMA radio access technique with the coexistence of GEO satCom network. In particular, this work aims to maximize the sum rate of LEO satCom by simultaneously optimizing the power budget over different beams, RsMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO satCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. second, for any given resource block user assignment, we adopt Karush–Kuhn–Tucker (KKT) conditions to calculate the transmit power over different beams and RsMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this wo

关键词： spectrum efficiency

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synergetic scheme for Digital Image Watermarking 1

Synergetic Scheme for Digital Image Watermarking

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1st IEEE bangalore Humanitarian Technology Conference, b-HTC 2020

作者： Ghuli, Anand biradar, Pandurang B.L.D.E.A's V.P.Dr.P.G.Halakatti College of Engineering and Technology. Department of Computer Applications Karnataka586103 India

ISBN: (纸本)9781728187945

The proposed scheme uses blind and non-blind digital color image watermarking with hybrid transformation. An image which is binary in nature is embedded in to color image with the use of the technique blind watermarking. The blind watermarking technique makes use of 'Discrete Wavelet Transformation' (DWT-2-level Haar Wavelets). To locate correct place to embed the watermark HL bands are considered. The conversion of spatial domain to frequency is done with Fast Fourier Transformation (2-D FFT). Then with the help of pseudo random sequence, block wise the scanning of host image is done and watermark is also embedded at the same time into predefined binary sequence components. Then non-blind technique embeds first watermarked image into second host image. DMeyer DWT is applied to have approximation coefficients for critical components. The combination of 'Discrete Fourier Transformation' (DFT) and 'singular value Decomposition' (svD) are applied to get sustainable components of the transformed image. From this combined approach we achieved good fidelity and robustness against-'Rotation', 'Cropping', 'JPEG compression' and for noises 'salt pepper', 'Gaussian', 'speckle' and multiple noises. © 2020 IEEE.

关键词： singular value decomposition

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Trajectory Planning of Multiple Dronecells in vehicular Networks: A Reinforcement Learning Approach

IEEE Networking Letters

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IEEE Networking Letters 2020年第1期2卷 14-18页

作者： samir, Moataz Ebrahimi, Dariush Assi, Chadi sharafeddine, sanaa Ghrayeb, Ali Concordia Institute for Information Systems Engineering Concordia University MontrealQCH4V1S1 Canada Department of Computer Science Lakehead University Thunder BayONP7B 5E1 Canada Department of Computer Science and Mathematics Lebanese American University Beirut1102 2801 Lebanon Electrical and Computer Engineering Department Texas AandM University at Qatar Doha Qatar

The agility of unmanned aerial vehicles (UAvs) have been recently harnessed in developing potential solutions that provide seamless coverage for vehicles in areas with poor cellular infrastructure. In this letter, multiple UAvs are deployed to provide the needed cellular coverage to vehicles traveling with random speeds over a given highway segment. This letter minimizes the number of deployed UAvs and optimizes their trajectories to offer prevalent communication coverage to all vehicles crossing the highway segment while saving energy consumption of the UAvs. Due to varying traffic conditions on the highway, a reinforcement learning approach is utilized to govern the number of needed UAvs and their trajectories to serve the existing and newly arriving vehicles. Numerical results demonstrate the effectiveness of the proposed design and show that during the mission time, a minimum number of UAvs adapt their velocities in order to cover the vehicles. © 2019 IEEE.

关键词： Trajectories

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Grain refinement process of AZ61 alloy by plastic deformation

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Materials Today: Proceedings 2024年

作者： Rajalaxmi Hegde s. Mohanasundaram s. Krishnaraj s. Arunkumar M. Udayakumar D. suresh Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karnataka India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Mechanical Engineering Sri Sai Ram Engineering College West Tambaram Chennai India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sankunthala Engineering College Avadi-Veltech Road Chennai India University of Technology and Applied Sciences Salalah Oman Department of Mechanical Engineering V.S.B Engineering College Karur Tamil Nadu India

Grain refinement is a pivotal technique for enhancing the mechanical attributes of magnesium alloys, offering improvements in strength, ductility, and fatigue resistance. Among the methods for achieving grain refinement, severe Plastic Deformation (sPD) stands out due to its effectiveness in imposing extreme strains on materials, either at room temperature or under moderately elevated thermal conditions. This process significantly alters the microstructure by transforming larger grains into much finer ones, thereby augmenting the alloy’s mechanical performance. The preparatory treatment of an alloy prior to sPD plays a critical role in determining the resultant microstructure and its associated mechanical properties. Although various preliminary treatments have been investigated for their impact on magnesium alloys, the specific influence of these treatments on the grain refinement of the AZ61 alloy necessitates further exploration. This gap in knowledge highlights the need for rigorous research to comprehend how different initial treatments, such as pre-annealing, influence the effectiveness of subsequent sPD processes. This study aims to meticulously examine the impact of various initial treatments on the grain refinement of AZ61 alloy through sPD. by systematically investigating these effects, the research seeks to deepen our understanding of microstructural evolution in AZ61 alloy and to derive strategies for optimizing processing routes that enhance its mechanical properties. The findings from this investigation are expected to significantly advance the application of AZ61 alloy in critical engineering domains where the demand for lightweight yet high-performance materials is crucial. This research could therefore have substantial implications for industries such as aerospace and automotive, where optimizing the balance between weight and strength is paramount.

关键词： AZ61 alloy Magnesium alloys Grain refinement severe plastic deformation Initial treatment

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space for All in Canada: Expanding Outreach across the Nation 73

Space for All in Canada: Expanding Outreach across the Natio...

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73rd International Astronautical Congress, IAC 2022

作者： Nadeem, sobia Trozzo, sophia de Korte, Marieke Dyanatkar, sepand Kunitskya, Alina Andres, Chimira Canada Department of Engineering Physics McMaster University 1280 Main St W HamiltonONL8S 4L8 Canada Department of Anatomy and Cell Biology Schulich School of Medicine and Dentistry Western University LondonONN6A 3K7 Canada Department of Laboratory Medicine and Pathobiology Temerty Faculty of Medicine University of Toronto 27 King's College Circle TorontoONM5S 1A1 Canada Department of Computer Science The University of British Columbia 2366 Main Mall VancouverBCV6T 1Z4 Canada Michael Smith Laboratories The School of Biomedical Engineering The University of British Columbia 2185 E Mall VancouverBCV6T 1Z4 Canada Lassonde School of Engineering Department of Earth and Space Science and Engineering York University 4700 Keele St TorontoONM3J 1P3 Canada

The space community in Canada has been growing exponentially in both academic and industrial sectors, especially in regions with access to aerospace programs, space-related organisations, and industry professionals. However, there is a known lack of connectivity to the national space community outside of these regions, particularly in Northern and Atlantic Canada. specifically, the students and young professionals in these underrepresented regions lack exposure to opportunities to engage with the Canadian space sector, resulting in barriers to entry. To combat this issue, the students for the Exploration and Development of space Canada (sEDs-Canada) has curated deliberate outreach opportunities for underserved communities. These programs include our 1) student space Organisation Affiliates, 2) student Chapters, and 3) Regional Representative programs. To directly target regions with less access to space (i.e. Northern and Atlantic Canada), sEDs-Canada has developed the following initiatives: 1) Canadian Arctic Research Expedition: Mars in Your Own backyard and 2) NAsA space Apps Hackathon (Yukon Territories 1st Edition and st. John's). To evaluate the impact of these programs, twelve (12) interviews and one (1) national survey was conducted. In addition, a strengths, Weaknesses, Opportunities, and Threats (sWOT) analysis was conducted with sEDs-Canada's Regional Representatives to evaluate the programs effectiveness and identify areas for growth for the outreach programs. Through these interviews, surveys and the sWOT analysis, participant demographics, personal anecdotes, and constructive feedback was collected. This paper will discuss the results of the aforementioned analyses to demonstrate the impact of sEDs-Canada's programming across Canada and in underserved communities. Additionally, the results of this analyses can be utilised to inform curating a strong national presence in the space sector in light of the identified challenges. Ultimately, our results wil

关键词： students

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