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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Waghale, Atharva Potdukhe, Nayan Rewatkar, Rajendra Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

In it, they contended that with the rise of artificial intelligence (AI) in the gaming field, the later has become more than mere algorithms. This review categorizes the milestones in the development of AI from basic algorithms and moving up to the current movement of machine learning and neural networks where AI agents can learn, reason on their own, and respond to the actions of the player. In this paper, seminal points in gaming history, technology progress, and essential artificial intelligence technologies constituting modern gaming are described. It combines the possible ethical issues related to artificial intelligence applications in the gaming industry. Measuring experiences and competitions that are thrilling and exciting but still fair. By outlining the current state and future possibility of artificial intelligence, this article explains how it has brought the change in gaming and has made it more real. © 2024 IEEE.

关键词： Adversarial machine learning

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AFHO-DL: Enhancing Energy Efficiency through Resource Allocation AI-enabled WSNs and IoT Integration 3

AFHO-DL: Enhancing Energy Efficiency through Resource Alloca...

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

作者： Kalaiselvan, S.A. Manoranjini, J. Hemalatha, S. Kumar, M. Lenin 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

In the realm of AI-enabled Wireless Sensor Networks (WSNs) and Internet of Things (IoT) integration, efficient resource allocation is paramount for enhancing energy efficiency and optimizing data utilization. The dynamic nature of environments poses challenges to achieving optimal performance measures, necessitating sophisticated solutions. To address this, we propose the AFHO-DL model, which combines the Fire Hawk Optimizer (FHO), UNet architecture, and Multi-objective Jaya algorithm. The FHO algorithm, inspired by the foraging behavior of Fire Hawks, efficiently optimizes resource allocation in WSNs by iteratively updating node positions based on objective functions. Leveraging FHO, we fine-tune the parameters of the UNet architecture, a deep learning model widely used in image processing applications. The UNet architecture, consisting of encoder and decoder paths, extracts features and reconstructs images, enhancing adaptability to various data types. Furthermore, the Multi-objective Jaya algorithm is employed to refine solutions by striking a balance between exploration and exploitation in the solution space, further improving resource allocation strategies. Our experimental results demonstrate the effectiveness of the proposed AFHO-DL model in enhancing energy efficiency and optimizing data utilization in dynamic environments. Through post-processing and visualization, we evaluate the performance of the optimization algorithms, generating Pareto-optimal solutions that represent the best trade-offs between competing goals. The AFHO-DL model facilitates intelligent decision-making and resource allocation strategy optimization, ensuring smooth data transfer and improving network efficiency in AI-enabled WSNs coupled with IoT platforms. © 2024 IEEE.

关键词： Deep learning

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Investigating Youtube's Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube's Educational Videos: Patterns, User C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sundar, Koushik Eugene Berna, I. Vijayakumar, R. Ambeth Kumar, V.D. Vijay, K. Citibank NA Jersey CityNJ United States Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Mizoram University Department of Computer Engineering Mizoram Aizawl India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331522988

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video's popularity was greatly influenced by the timing of the content's release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops. © 2024 IEEE.

关键词： Video analysis

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AI on the Edge: A Novel Approach to Detect Waste on Water Bodies 8

AI on the Edge: A Novel Approach to Detect Waste on Water Bo...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Prasad, Ishan Shekhar Sanketh, Medha Chandra Kumar, R. R.V College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India R.V College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9798331505462

Marine environments are being increasingly burdened by waste from heavy industries and the large scale disposal of electronics and plastics into waterways that flow into the oceans. Recent advancement in technologies like real time image processing, machine learning and edge computing has made it feasible to develop commercially viable solutions for automated identification of garbage on water bodies. This paper explores image processing as a targeted approach for the on-field identification of water pollutants. A unique aspect of this approach was to demonstrate the effectiveness of its implementation on standalone edge devices viz. ones that could be used in remote locations with limited compute capabilities and no wireless connectivity. The study demonstrates this use-case with TensorFlow as an AI framework and Raspberry Pi 4B as an edge device. The model achieved a mean Average Precision (mAP) of 51.20%, validating its performance in identifying water pollutants. © 2024 IEEE.

关键词： Plastics industry

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Con-Fog: Consensus-Driven Fog Node Selection in FU-Serve Platform for IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 24686-24697页

作者： Imandi, Raju Roy, Arijit Sethi, Kamalakanta Pavan, Kumar Guizani, Mohsen Indian Institute of Information Technology Computer Science and Engineering Group Sri City India Indian Institute of Technology Department of Computer Science and Engineering Patna India Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

The rapid expansion of Internet of Things (IoT) devices and applications necessitates the need for more efficient computational and data management strategies. The Fog-enabled UAV-as-a-Service (FU-Serve) platform addresses these demands by integrating fog computing to enhance the operational efficiency of UAVs in IoT environments. Despite its advantages, the FU-Serve platform faces significant challenges, including data transmission latency, resource allocation, and energy management, contributing to the underutilization of UAVs and fog nodes. To address these challenges, this paper introduces a consensus-driven approach, Con-Fog, that optimizes the selection of fog nodes for UAVs within the FU-Serve platform. Con-Fog evaluates potential fog nodes within the communication range by computing utility values based on geographical distance, link quality, available computational resources, and residual energy. UAVs rank these nodes according to their utility values and select the most suitable ones through a consensus-based approach, ensuring alignment with the operational demands of IoT devices. Additionally, we apply an optimal best-fit algorithm to refine fog node allocation, maximizing resource utilization while keeping it below each node’s capacity threshold (T%). Our simulation results show that Con-Fog significantly enhances key IoT performance metrics. Transmission time and the number of unassigned UAVs decrease by 10%-30% and 20%-40%, respectively, while residual energy increases by 30%-50% compared to existing systems. These improvements enhance the management of UAV and fog node resources, thereby advancing the effectiveness of IoT applications within the FU-Serve platform. © 2014 IEEE.

关键词： Fog computing

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Addressing Data Imbalance in machine learning: Challenges and Approaches 6

Addressing Data Imbalance in Machine Learning: Challenges an...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Langote, Meher Zade, Nikita Gundewar, Swapnil Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Sawangi India

ISBN: (纸本)9798331522667

A key difficulty of dealing with imbalanced datasets is that machine learning models are trained to favor the majority class and therefore perform poorly when it comes to a minority class classification. In this paper, we consider the following methods of tackling the issue of the over-representation of a class;random oversampling, undersampling, SMOTE, cost-sensitive learning, and ensemble methods. Comparing them, we assess the performance of each of these techniques within different classifiers. Techniques including SMOTE together with combined methods including Random Forests also show higher capacity in minority class predictions. However, problems such as overfitting and the increase in the computational load are not escapable yet. We briefly recap the strengths and weaknesses of each approach and propose potential refinements including a combination of both methods and dynamic resampling methods to serve as the next steps toward improving the model. Thereby, our results draw attention to the necessity of accurate methods for handling the class imbalance, which will further yield more accurate and fair machine learning solutions across a variety of applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Story Generation Using GAN, RNN and LSTM 1

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13th International Advanced Computing Conference, IACC 2023

作者： Shrouti, Devika Bayas, Ameysingh Joshi, Nirgoon Misal, Mrinank Mahajan, Smita Gite, Shilpa Computer Science and Engineering Department Symbiosis Institute of Technology Pune India Artificial Intelligence and Machine Learning Department Symbiosis Institute of Technology Pune India

ISBN: (数字)9783031567001

ISBN: (纸本)9783031566998

This paper explores the domain of story generation and presents a novel approach that uses Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. The objective is to generate realistic and engaging stories for children. The traditional language models are proficient in maintaining grammatical consistency but often fail to establish long-term coherence. This study addresses and explores the individual performances and capabilities of 3 distinct text generation models based on GAN, RNN & LSTM respectively. The project employed three individual models and trained them on the same dataset and the evaluation was conducted using METEOR scores, accuracy, and loss metrics. To address this, our study introduces the use of GANs to enhance the quality of synthetic text. The MaskGAN model gave the highest accuracy and decent output on the trained dataset followed by the RNN and LSTM models. This paper is a significant step forward for story generation, highlights the unique contributions of GANs, RNNs, and LSTMs, amplifies the consistency and quality of independently generated narratives, and provides a foundation for future comparative analyses. MaskGAN achieved the highest accuracy and excelled in generating realistic and high-quality narratives followed by RNNs which exhibited decent accuracy but faced challenges with longer narratives, while the beam search-enhanced LSTM improved narrative quality offering a promising solution for coherent story generation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Long short-term memory

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A Framework for Picture Categorisation Primarily Examines the Possibilities of ELM

A Framework for Picture Categorisation Primarily Examines th...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Nagendrudu, P.V.V.S.D. Sekhar, Chintam Chandra Kandru, Jaya Lakshmi Revanth, Pasupuleti Venkata Department of Artificial Intelligence and Machine Learning Aditya Engineering College Andhra Pradesh Surampalem India Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India

ISBN: (纸本)9789819965465

A hybridised image classification technique based on discrete wavelet auto encoder, principal component analysis, and extreme learning machine is proposed. The suggested approach is split into three stages: (a) The subsequent stage in the data preprocessing phase is to extract features from the preprocessed pictures. This may be performed through the use of several approaches such as object recognition, image segmentation, and extraction of features with the help of deep learning models. (b) Principal component analysis to transform a dataset into a lower-dimensional space while retaining the most significant properties of the data. PCA can help simplify huge dataset processing and minimise the computational complexity of machine learning methods. (c) Following feature extraction, the features are sent into extreme learning machines (ELMs) for categorisation. The hidden layer employs a random matrix to translate the input parameters to a higher dimensional space, while the output layer conducts classification based on the mapped features. Therefore, it can be stated that PCA-ELM provides higher generalisation performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Principal component analysis

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