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检索条件"机构=Artificial Intelligence and Machine Learning Faculty of Engineering and Technology"

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Shruthi Bennur Mallikarjuna Channakrishna Raju Department of Computer Science and Engineering SJCE JSS Science and Technological University Mysuru India Department of Artificial Intelligence and Machine Learning Sri Siddhartha Institute of Technology SSAHE Tumakuru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Wireless Sensor Networks (WSN) are designed to monitor sensor nodes systems, with a focus on data processing, collection and transmission. Developments in wireless communication have extended the scope of WSNs to various domains such as underwater communication and Internet of Things (IoT). However, secure clustering and routing remain challenging due to increased vulnerability to attacks in IoT-WSN, which negatively impact communication and data integrity. This research proposes Similarity Preference Weight-Chimp Optimization Algorithm (SPW-ChOA) for secure clustering and routing in WSN-IoT. Trust, node degree, distance between Cluster Head (CH) and Base Station (BS), energy and distance are employed as fitness functions for Secure CH (SCH) selection. The factors considered for routing in SPW-ChOA include energy and distance. The performance of SPW-ChOA is evaluated in terms of residual energy, Packet Delivery Ratio (PDR), throughput and energy consumption, with the number of nodes varied at 500,1000,1500 and 2000. The proposed SPW-ChOA consumes lesser energy at 0.28 which compared to the existing Butterfly Optimization Algorithm (BOA) and Antlion Optimization Algorithm (AOA).

关键词： Wireless communication Wireless sensor networks Underwater communication Energy consumption Clustering algorithms Routing Throughput Internet of Things Optimization Monitoring

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Optimizing FAANG Stock Forecasting: The Power of Feature engineering and LSTM in Financial Analysis

Optimizing FAANG Stock Forecasting: The Power of Feature Eng...

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2025 International Conference on artificial intelligence and Data engineering, AIDE 2025

作者： Tiwari, Priyanshu Patil, Soumya Ambareesh, S. Raj, Rishabh Sir M. Visvesvaraya Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Sir M. Visvesvaraya Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331527518

The unprecedented growth and market capitalization of the FAANG stocks - Facebook, Amazon, Apple, Netflix, and Google (Alphabet) - have mushroomed into the pay-off race of the leaders of the global financial market. Stock price volatility prediction attracts the focus of the investors and analysts in the pursuit of profit maximization relying on market movements. In order to enhance the precision of stock price predictions for FAANG companies, other more intricate methods for feature engineering are also coupled together with LSTM neural networks. Stock price dynamic intricacies are being reflected by the model using the complete set of financial indicators, predictive analytics, and market sentiment data as features. The work showed a phenomenal application of LSTM networks in the analysis and prediction of stock price trends compared to the classic methods on the sequential-by-nature front dealing with long dependencies. It is actually possible to build a strong base of data enabling the detection of such elusive trends in the performances of FAANG stocks and technical indicators, sentiment analysis, or economic metrics, for instance, thereby getting into working order for LSTM modeling. Results from experiments show the method provides better accuracy and reliability than conventional machine learning models, thus collecting valuable insights for certain financial-forecasting purposes. The paper serves as a demonstration illustrates how feature engineering and LSTM might be combined to improve predicting financial markets and provides one solution for investors, financial analysts, and policymakers who look to make informed investment decisions. © 2025 IEEE.

关键词： Deep learning

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Advances in Glaucoma Detection Through machine learning technology

Advances in Glaucoma Detection Through Machine Learning Tech...

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artificial intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Rushikesh Burle Sanskruti Gaurkhede Leelkanth Dewangan Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

In the event that glaucoma is not treated promptly, blindness may become permanent. It ranks second among the factors that might lead to blindness in the eyes. One example of a recent imaging technology that looks within the eye is the fundus camera. Glaucoma is diagnosed using a variety of techniques, such as the retinal nerve fibre layer analyzer, The Topcon image net technique and optical coherence tomography. Despite its expensive cost and the dearth of available research in this field, the optic cup to disc ratio is used to diagnose glaucoma. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The beaker slowly gets better and inhabits most of the judgement as the glaucoma becomes worse. If glaucoma is not pickled trendy a timely manner, blindness may become irreversible. It is the second most common cause of ocular blindness. A contemporary imaging tool called the fundus camera is utilised to view within the eye. Among the methods used to diagnose glaucoma include the retinal nerve fibre layer analyzer, optical coherence tomography, and the Topcon image net approach. Despite its high cost and paucity of research, glaucoma is diagnosed using the optic cup to discus relation. The degree of glaucomatous damage is mostly determined by the appearance of the optic cup. The cup expands as glaucoma progresses until it finally occupies the majority of the eye.

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Congestion Control Routing Scheme for WSN using AI Technologies 3

Congestion Control Routing Scheme for WSN using AI Technolog...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Basha, S.K. Mahaboob Gurumoorthi, E. Beulah, E. Mercy Kesavan, R. Kalaiselvan, S.A. Kumar, Lenin Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

An essential part of wireless sensor networks (WSN) is managing congestion, which occurs when the amount of traffic exceeds the combined or individual capacities of the underlying channel. As a result, developing more advanced methods to prevent, detect, and alleviate congestion requires particular considerations. In order to obtain the highest throughput, these strategies must take into account the limited resources of the WSN. Dedicated congestion control strategies and routing protocols that help in congestion detection as well as management are two of the many methods that have been created in recent years. As a means of avoiding congestion, the sink node in the previous designs would reset the topology and reduce bulk traffic. In this article, we lay the groundwork for WSNs and AI, the latter of which will try to prevent congestion in WSNs from the get-go and the former of which will try to minimize the spread of impending congestion by rerouting traffic. An integral part of modern life, artificial intelligence (AI) allows us to create self-governing systems. It is possible to enhance performance and reliability by making use of several sophisticated AI approaches. Better network management and routing in WSN may be the outcome of studying the AI algorithm used by WSN, leading to a more dependable network. Here we'll take a look at several AI algorithms that have found their way into WSNs, and we'll talk about how these algorithms might help WSNs overcome their challenges and become more reliable and efficient. Additionally, artificial intelligence has often been disregarded in these networks because of limitations on data processing as well as power consumptions. Thanks to AI, WSN is now lightning fast, error-free, as well as effective in almost every way. Using AI methods in nodes of wireless sensors for congestion aware routes is the goal of this work. © 2024 IEEE.

关键词： Traffic congestion

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Enhancing Fare Prediction Accuracy in Ride Hailing Through Neural Networks and Data Simulation

Enhancing Fare Prediction Accuracy in Ride Hailing Through N...

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artificial intelligence and Data engineering (AIDE), International Conference on

作者： Priyanshu Tiwari S Ambareesh Soumya Patil Rishabh Raj Dept. of Artificial Intelligence and Machine Learning Sir M. Visvesvaraya Institute of Technology Bengaluru India Dept. of Information Science and Engineering Sir M. Visvesvaraya Institute of Technology Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Prediction of fare is the need for better service quality and effective pricing strategies that make for enhanced customer experiences in a ride-hailing service. From the identified features based on rides and ride-hailing activities - the distance covered, the time used for travel, number of passengers, and the main purposes of trips, the researchers will create a neural network model using such synthesized data to make real-time predictions for an accurate total fare estimate. This synthesis helps to understand the noise and natural variability in the ride-hailing datasets by showing different interaction patterns of the features over time. The analysis techniques used include scatter plots, correlation matrices, and time-series evaluations that reveal strong connections between fare amounts and distance traveled and trip duration. The dataset used in this research was sourced from Kaggle, ensuring a reliable foundation for training and evaluating the model. The proposed neural network architecture achieved 90% accuracy, with Mean Absolute Error (MAE) of 1.5 units and Root Mean Squared Error (RMSE) of 2.3 units, demonstrating its reliability. This paper shows how neural networks might be applicable to fare predictions and, therefore, can contribute toward artificial datasets with practical models applicable in the real world.

关键词： Training Analytical models Adaptation models Accuracy Neural networks Pricing Predictive models Data models Tides Synthetic data

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Dynamic Channel Allocation Using Reinforcement learning Algorithm for Multiple Input Multiple Output Systems

Dynamic Channel Allocation Using Reinforcement Learning Algo...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Srinivasa Rao Bittla Raami Riadhusin Divyaraj G N S. Aravindh Ahila B Independent Researcher Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, Multiple-Input Multiple-Output systems (MIMO) play a crucial role in modern wireless networks by enhancing spectral efficiency and data rates. Traditional static, heuristic-based allocation methods struggled to adapt to real-time variations, handling high-dimensional state spaces, minimizing interference, and ensuring fairness in channel allocation. However, decision-making in dynamic environments requires intelligent and scalable approach that enables real-time decision-making by efficiently learning and adapting to changing network conditions. To overcome these issues. This research proposed Deep Q-Network with Prioritized Experience Replay (DQN-PER) for dynamic channel allocation for MIMO Systems. DQN-PER prioritizes high-value learning experiences and while also improving training efficiency and convergence speed. PER prioritizes important experiences based on Temporal-Difference (TD) error by allowing the model to focus on more informative transitions, which speeds up learning and improves adaptability. Simulation results demonstrate enhanced spectral efficiency, reduced packet loss, and improved Quality of Service (QoS) in dynamic wireless environments. These findings establish DQN-PER as a promising solution for intelligent and adaptive MIMO resource allocation. The proposed method achieved better results in terms of a packet drop rate 0.04 compared to existing method Advantage Actor-Critic (A2C).

关键词： Spectral efficiency Heuristic algorithms Wireless networks Decision making Quality of service Dynamic scheduling Real-time systems Resource management Optimization Convergence

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From 5G to 6G: The Role of AI, machine learning, and Deep learning in Wireless Systems

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Pradnyawant M. Gote Praveen Kumar Prateek Verma Prajyot Yesankar Adesh Pawar Saniya Saratkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible.

关键词： 6G mobile communication Wireless communication Deep learning Tactile Internet Technological innovation Sentiment analysis 5G mobile communication Reviews Heuristic algorithms Security

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Dynamic Trust Management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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Enhancing Fare Prediction Accuracy in Ride Hailing Through Neural Networks and Data Simulation

Enhancing Fare Prediction Accuracy in Ride Hailing Through N...

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2025 International Conference on artificial intelligence and Data engineering, AIDE 2025

作者： Tiwari, Priyanshu Ambareesh, S. Patil, Soumya Raj, Rishabh Sir M. Visvesvaraya Institute of Technology Dept. of Artificial Intelligence and Machine Learning Bengaluru India Sir M. Visvesvaraya Institute of Technology Dept. of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798331527518

关键词： Mean square error

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The Accurate Prediction and Forecasting Strategy Implementation for Various Weather Conditions using Different Algorithms 4

The Accurate Prediction and Forecasting Strategy Implementat...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Sivapriyan, R. Vidya Prabha, C.V. Santhakumar, D. Rao, Kolli Himantha Mohammed, Safaa Hallol Abdtawfeeq, Tareq Hafdhi Sir Mvit R & D Centre Department of Eee Bengaluru India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Engineering Chennai India Institute of Cse Saveetha Engineering College Department of Artificial Intelligence and Machine Learning India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

this study presents a new regression method for forecasting the occurrence of rainfall. The method includes three main steps: feature extraction, classification, and preprocessing. In the feature extraction step, all weather samples are normalized for a uniform sampling frequency, after which signal noise is removed to improve data quality and each signal is then converted to a 1 × 34 feature vector, capturing subtle emotional nuances. Notably, normalization precedes classifier training to improve model performance. Performance metrics including f-score, sensitivity, precision, and accuracy are used to evaluate the efficiency of the model. The proposed methodology has the potential to advance our understanding of emotional responses to music, providing a promising avenue for future research in predicting rainfall and other natural phenomenon. © 2024 IEEE.

关键词： Prediction models

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