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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Geetha Rani, E. Tukkoji, Chetana Anusha, D. Dhanalakshmi, M. Bharath, B. Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Gitam University Karnataka Bengaluru India Department of CSE- Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Canteen Stores Department MVJ College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture relies substantially on water. The IoT-based farm water management system explains how IoT technology can control irrigation intelligently. This system may be used to collect data by connecting several sensors such as soil moisture, temperature, and PIR motion sensors, among others. The collected data will be transferred to the user's mobile phone and sent to the users, ensuring crops receive the appropriate quantity of water and optimize irrigation schedules. By monitoring water levels, farmers may detect possible water loss locations and take action to prevent them. The primary goal of our article is to increase productivity, efficiency, and water use through live monitoring, water management, crop growth development, and the potential use of virtual water. Furthermore, breakthroughs in machine learning, such as crop prediction and recommendation, fertilizer detection, and leaf disease detection, will allow for more complex analysis of agricultural data, resulting in even great gains in water management and crop yield. However, it is important to ensure that these technologies are accessible to all farmers, regardless of the location or resources. If giving detailed information on soil properties and crop genetics, machine learning algorithms can forecast agricultural yields and find the best crops for a giving region. Now, farmers can maximize their crops by making data-driven decisions. This one system’s capability to exactly predict crops yields and resources requirements be one of its key advantages. To promote more sustainable and productive farming, farmers should also control the quantity of water and other resources used by them. By assuring a sufficient supply and reducing the likelihood of crops failure, crops forecast, leaf disease diagnosis, and guidance systems can assist address concern related to global food securities in addition to enhancing productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 20

关键词： Soil moisture

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Stock Price Prediction using LSTM and TLBO

Stock Price Prediction using LSTM and TLBO

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar M, Kishore Veena, R.C. Ila Chandana Kumari, P. Shivakanth, Gandla Rai B, Karunakara Cmr Technical Campus Department of Cse Hyderabad India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science of Engineering Telangana India K L University Vadeeshwaram Dept of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The stock market's inherent complexity and volatility make accurate predictions difficult. Complex patterns in unstructured data may be simulated and analyzed with the help of Deep Learning. It is possible to employ deep learning models for a wide variety of purposes, including but not limited to image recognition, audio recognition, and natural language processing (NLP). As a result, everyone with a stake in the market is always trying to make educated guesses about what the stock price will do next. We used many deep learning techniques, including Long Short-Term Memory, to model our problem (LSTM). To successfully predict the closing prices of three distinct markets, an Extreme Learning Machine (ELM) model is combined with a variety of metaheuristic optimization techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Symbiotic Organism Search (SOS). These algorithms are helpful in creating ELM network weights and biases in the most effective way possible. The Teaching and Learning Based Optimization (TLBO) paradigm underpins most of the models and methodologies now in use for feature selection. To that end, this study sets out to identify the most effective machine learning model for making such predictions. The primary objective is to develop a smart technology that can predict the movement of stock market values based on financial time series and news headlines. Investors would be able to make more informed decisions based on the binary results of the proposed method. Processing time, error rate, and accuracy in forecasting the direction of stock price movement for five companies across sectors are used to evaluate the performance of the proposed LSTM-TLBO model. © 2023 IEEE.

关键词： Long short-term memory

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Design of Multiple Share Creation with Optimal Signcryption based Secure Biometric Authentication System for Cloud Environment

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International Journal of computers and Applications 2022年第11期44卷 1047-1055页

作者： Prabhu, D. Vijay Bhanu, S. Suthir, S. Department of Computer Science and Engineering Annamalai University Chidambaram India Research Supervisor & Associate Professor Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

Biometric authentication plays a vital role in the cloud computing (CC) environment to accomplish security and privacy. Several kinds of biometrics are used for the authentication process, particularly, the fingerprint is a commonly used biometric, which accomplishes maximum security. But it can be easily stolen, unintentionally revealed, or difficult to remember. Therefore, biometric authentication techniques are needed and an encryption process is included to improve the security of the authentication process. In this aspect, this paper proposes a multiple share creation with an optimal signcryption-based biometric authentication system (MSCOS-BAS- FLNN- PeSeOA) for the cloud environment. The proposed model enables the creation of multiple shares of the input biometric image (fingerprint) and encrypts them for a secure authentication system. In addition, the encryption of individual shares takes place with an optimal signcryption system where the key generation process is carried out using the penguin search optimization algorithm (PeSeOA). Finally, the functional link neural network (FLNN) model is applied for the biometric authentication verification process. A wide range of simulation analyses are performed to highlight the supreme efficacy of the MSCOS-BAS- FLNN- PeSeOA technique. The experimental outcomes pointed out the hopeful performance of the MSCOS-BAS- FLNN- PeSeOA technique over the recent techniques. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Authentication

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Dynamic Population-Based Jaya Algorithm for Genetic Biomarker Classification 21

Dynamic Population-Based Jaya Algorithm for Genetic Biomarke...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Baliarsingh, Santos Kumar Prasad Dev, Prabhu Bhoi, Ashutosh Mohanty, Jasaswi Prasad Hota, Jhalak KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India GITAM Deemed to Be University Department of Computer Science & Engineering Visakhapatnam530045 India

ISBN: (纸本)9798350358230

Cancer is among the leading causes of death worldwide. If we can predict an individual's risk of developing cancer, then steps can be taken to minimize the risk of developing a malignancy. One of the useful techniques is to identify the genetic biomarkers that are responsible for causing cancer. Detection and classification of these biomarkers require either biopsy computer-aided imaging or microarray data analysis. This paper proposes a new technique for the detection and classification of biomarkers using microarray data analysis. The work is based on a hybrid method where gene selection is carried out using minimum redundancy maximum relevance (mRMR) followed by a dynamic population-based Jaya algorithm. Here logistic map is employed for faster convergence. The majority of the existing evolutionary algorithms (EAs) are based on fixed population size which increases computation time. Therefore, a dynamic population-based evolutionary algorithm is employed here. Specifically, the goal is to investigate if there is a significant advantage of reducing the population size with the increase in iterations. To validate the performance of the proposed method, three state-of-the-art algorithms are selected for comparison over three benchmark datasets. The results of our experiments prove that our proposed method performs better than fixed population-based methods. © 2023 IEEE.

关键词： Biomarkers

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Glioma Segmentation in MR Images Using 2D Double U-Net: An Empirical Investigation 10th

Glioma Segmentation in MR Images Using 2D Double U-Net: An ...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Webber, Julian L. Nancy Noella, R.S. Mehbodniya, Abolfazl Ramachandran, V. Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Doha Kuwait Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Glioma develops inside the brain. Automatic glioma segmentation reduces computational time and increases survival owing to previous treatment preparations—2D double U-Net segments glioblastoma from multi-modal MRI images (MRI). 2D brain models need fewer computational resources than 3D models. Residual U-Net (RU-Net) and dilated convolution U-Net make up the Double U-Net design (RU-Net). RU-residual Net’s route contains three 33 convolution kernels. DU-Net contains three 33 and 55 convolution kernels with a two dilation rate. RU-Net is more uncomplicated to optimise than deep networks and makes fewer training mistakes than deep networks. DU-Net gathers contextual information and creates details. The suggested model was assessed using the BraTS 2017 training dataset and attained dice similarity coefficients of 0.91, 0.85, and 0.88 for total, enhanced, and tumour core (TC), respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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A Novel Drone-Station Matching model in Smart Cities based on Strict Preferences

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无人系统（英文） 2023年第3期11卷 261-271页

作者： Debolina Nath Anjan Bandyopadhyay Ankit Rana Tarek Gaber Aboul Ella Hassanien Department of Computer Science and Engineering Amity University Kolkata KolkataWest BengalIndia Department of Computer Science and Engineering Gitam University BangaloreKarnatakaIndia School of Science Engineeringand Environment University of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailiaEgypt Scientific Research Group in Egypt(SRGE) Egypt Faculty of Computer and Artificial Intelligence Cairo UniversityCairoEgypt Scientific Research Group in Egypt(SRGE) Egypt

There has been a considerable increase in the use of drones,or unmanned aerial vehicles(UAVs),in recent times,for a wide variety of purposes such as security,surveillance,delivery,search and rescue operations,penetration of inaccessible or unsafe areas,*** increasing number of drones working in an area poses a challenge to finding a suitable charging or resting station for each drone after completing its task or when it goes low on its *** classical methodology followed by drones is to return to their pre-assigned charging station every time it requires a *** approach is found to be inefficient as it leads to an unnecessary waste of time as well as power,which could be easily saved if the drone is allotted a nearby charging station that is ***,we propose a drone-allocation model based on a preference matching algorithm where the drones will be allotted the nearest available station to land if the station is *** problem is modeled as three entities:Drones,system controllers and charging *** matching algorithm was then used to design a Drone-Station Matching *** simulation results of our proposed model showed that there would be considerably less power consumption and more time saving over the conventional *** would save its travel time and power and ensure more efficient use of the drone.

关键词： Unmanned Aerial Vehicle(UAV) stable matching resource allocation smart city matching algorithm

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Leveraging Model Distillation as a Defense Against Adversarial Attacks Based on Deep Learning 2

Leveraging Model Distillation as a Defense Against Adversari...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Suresh, K. Radha, J. Thilagaraj, T. Subramani, R. Lakineni, Prasanna Kumar Taqui, Syed Noeman Department of Computer Science Bangalore India Nandha Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Moodlakatte Institute of Technology Department of Computer Science and Engineering Moodalakatte Karnataka Kundapura India Department of Mathematics Bangalore India Gitam School of Technology Gitam University Department of Computer Science and Engineering Visakhapatnam India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Adversarial attacks on deep learning models threaten machine learning system security and reliability. The above attacks use modest data alterations to produce erroneous model results while being undetected by humans. This work suggests model distillation to prevent adversarial perturbations. The student model is taught to emulate the teacher model in model distillation. This is done using teacher model soft outputs. Our idea is that this strategy organically strengthens the student model against adversarial assaults by keeping the teacher model's essential knowledge and generalization capabilities while reducing weaknesses. Distilled models are more resilient to adversarial assaults than non-distilled models, according to experiments. These models also perform similarly on undamaged, uncorrupted data. The results show that model distillation may be a powerful defense against machine learning adversaries. This method protects model resilience and performance. © 2023 IEEE.

关键词： Adversarial Attacks Deep Learning Defensive Approach Machine Learning Model Distillation

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Covid Analysis using Recurrent Neural Network

Covid Analysis using Recurrent Neural Network

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Padhi, Manoj Kumar Sahana, D.S. Madhavi K, Bindu Lal, Bechoo Rai B, Karunakara Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The examination of medical images has benefited greatly from the use of artificial intelligence. In contrast to deep learning systems, which do feature extraction automatically and without human interaction, traditional computer vision methods rely on manually produced features that are particular to a certain domain. Having access to medical information for automated analysis is another major factor driving the trend towards deep learning. Chest x-ray pictures are processed in order to segment the lungs and identify diseases in this thesis. Due to its cheap cost, ease of capture, and non-invasive nature, chest x-ray is the most often used medical imaging technology. However, automatic diagnosis in chest x-rays is difficult due to (1) the presence of the rib-cage and clavicle bones, which can obscure abnormalities that are located beneath them, and (2) the fuzzy intensity transitions near the lung and heart, dense abnormalities, rib-cages, and clavicle bones, which make the identification of lung contours subtle. In x-ray image processing, the Convolutional Neural Network (CNN) is the most often used deep learning architecture. Because to the enormous number of parameters in deep CNN architectures, intensive computing resources are required to train these models. Additionally, chest x-ray datasets are often rather tiny, and there is always the risk of overfitting when developing a model. In this dissertation, we propose five convolutional neural networks (CNNs) to identify illness and segment the lungs in chest x-rays. New Line, New Line In the first research paper, an adaptive lightweight convolutional neural network (ALCNN) is created to detect pneumothorax with few parameters. The model readjusts the feature calibration channel-wise using the convolutional layer and attention mechanism. The suggested model outperformed state-of-the-art deep models trained using three different transfer learning methods. One notable aspect of the suggested model is that it require

关键词： Convolutional neural networks

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Development of a Real-time Location Monitoring App with Emergency Alert Features for Android Devices 4

Development of a Real-time Location Monitoring App with Emer...

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Gowda, Dankan V. Prasad, K.D.V. Shekhar, R. Srinivas, Rachakonda Srinivas, Kale Naga Venkata Lakineni, Prasanna Kumar Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Pune India Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Department of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of CSE GITAM School of Technology GITAM University Visakhapatnam India

ISBN: (纸本)9798350305258

Smartphones have completely altered the mobile communication scene. Wi-Fi, global positioning system navigation, high-resolution cameras, and touchscreens with high-speed internet access are just some of the cutting-edge capabilities that these devices offer, allowing users to stay in constant contact with the present. Since many of these features are embedded deeply in the mobile operating system, they are typically inaccessible to the average user. However, Google released Android, a revolutionary operating system. Because of its open system architecture, this platform encourages third-party development and a debugging environment that users may change to create their own unique apps. In this research project, we examine the development of an Emergency Based Remote Collateral Tracking System app on the Android mobile platform from Google. There are three main forms of emergencies: those involving the heart, those involving personal safety, and those involving the roads. Users who own and operate motor vehicles are the primary focus of this app. Our program can keep tabs on the driver's pulse by connecting to a heart rate monitor. Our application has a backup function in case of anomalies. First, it sends SMS messages containing the user's location data after using GPS to do so. © 2023 IEEE.

关键词： Android application development Emergency alert system GPS tracking Location monitoring app Location sharing Real-time location tracking

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