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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-dept. exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 IEEE.

关键词： Genetic algorithms

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

引用

18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

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Cancer Death Cases Forecasting using Supervised Machine Learning 4

Cancer Death Cases Forecasting using Supervised Machine Lear...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Degadwala, Sheshang Vyas, Dhairya Kothari, Ankita Khunt, Urmila Sigma University Computer Engineering Department Gujarat Vadodara India University of Baroda The Maharaja Sayajirao Gujarat Vadodara India Krishna School of Emerging Technology and Applied Research Kpgu Dept. of Comp. Sci. & Eng. Vadodara India Itm Vocational University Computer Science Engineering Vadodara India

ISBN: (纸本)9798350300093

In India, like in the rest of the world, cancer is a major killer. This research objective is to predict cancer mortality in India, using supervised machine learning methods. Cancer mortality rates in India between 1990 and 2017 are provided by age group, gender, and region using data from the Global Burden of Disease Study. We employ three distinct supervised learning algorithms - linear regression, decision tree regression, and random forest regression - after performing data preprocessing, which includes missing value imputation and feature engineering. Using a variety of criteria, we analyze the effectiveness of these models and conclude that the random forest regression model is superior to the other two. The scope of research is provide a long-term prediction of cancer mortality in India using the best model so it will help health department to work on it. Our research has implications for policymakers and healthcare providers in India, where it may inform efforts to reduce cancer rates and improve cancer care. © 2023 IEEE.

关键词： Forecasting

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Efficient lightweight image compression approach for Multimedia Internet of Things 5

Efficient lightweight image compression approach for Multime...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Idrees, Sara Kadhum Couturier, Raphael Azar, Joseph Idrees, Ali Kadhum Gechter, Franck BelfortF-90000 France University of Babylon College of Information Technology Dept. of Information Networks Babylon Iraq Université Marie et Louis Pasteur CNRS Institut FEMTO-ST BelfortF-90000 France University of Applied Science and Arts Smart Edge Lab Faculty of Computer Science Dortmund Germany SIMBIOT Team Vandoeuvre-lès-NancyF-54506 France

ISBN: (纸本)9798331519957

The development of the Internet of Things (IoT) and communication technologies has led to the emergence of the multimedia IoT. The multimedia IoT contributes to a variety of multimedia applications, that can capture and transmit large amounts of images or video sequences over an IoT network. Due to restricted resources, multimedia IoT devices must transmit less multimedia data while retaining accuracy. This can save energy and memory use and increase multimedia IoT network performance. This paper proposes an efficient lightweight image compression approach (ELiCA) for Multimedia Internet of Things. The proposed ELiCA starts dividing the captured image into equal-sized blocks and the Discrete Cosine Transform (DCT) is applied to each of them to produce the DCT coefficients. Then, the DCT coefficients are quantized. The output of the quantization process is reordered using zigzag process. Finally, the output files of the zigzag are compressed using the error-bounded lossy compression technique known as SZ developed specifically for High-Performance Computing (HPC) systems. The compressed file will be transmitted by the MIoT device. The proposed ELiCA is evaluated using real sensed images and based on real IoT devices to show the efficiency of the proposed approach. The ELiCA presents better results compared with JPEG in terms of compression ratio, PSNR, and SSIM. © 2025 IEEE.

关键词： Image compression

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SITS-BERT ++: Enhancing Satellite Image Time Series Analysis with Novel Architectural and Methodological Innovations

SITS-BERT ++: Enhancing Satellite Image Time Series Analysis...

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Conference on Information and Communication Technology (CICT)

作者： Gade Sai Shravan Sai Kulvanth S Ashrith Raj S Vidyasager G R Bhaskaran V Kumar T Rajamani Swaroop Srisailam Dept. of Math and Computer Science Sri Sathya Sai Institute of Higher Learning Anantapur India Cropin AI Labs CropIn Bengaluru India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

In this study, we introduce SITS-BERT++, a novel adaptation of the SITS-BERT model tailored for the analysis of annual satellite observation time series data. Our approach integrates the Swish activation function and a sophisticated time warping data augmentation technique to significantly en-hance model performance. Additionally, we propose a multi-faceted architectural overhaul that includes dual activation functions-Swish for the overall model and GeL U for specific prediction and classification tasks-resulting in a substantial improvement in accuracy from 90.61 % to 93.07%. These inno-vations demonstrate the efficacy of SITS-BERT ++ in advancing the state-of-the-art in satellite time series analysis, surpassing the SITS analysis models.

关键词： Analytical models Adaptation models Satellites Accuracy Time series analysis Predictive models Data augmentation Data models Satellite images Remote sensing

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Exploration of Ripe Raw and Defective Tomatoes with Deep Learning Techniques 2

Exploration of Ripe Raw and Defective Tomatoes with Deep Lea...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Vanitha, C.N. Sathyamoorthy, Malathy Dhanaraj, Rajesh Kumar Rai, Priti Geetha, V. Manikantan, M. Kongu Engineering College Science and Engineering Department of Computer Erode India Kpr Institute of Engineering and Technology Department of Information Technology Coimbatore India Pune India Gl Bajaj Institute of Management Greater Noida India Reva University School of Applied Sciences Department of Computer Science Bangalore India Kongu Engineering College Dept. of CT-UG Perundurai India

ISBN: (纸本)9798350325799

Due to great need of agricultural operations, there was a shortage of workers during the harvest period. This results in a bottleneck in the operations of agricultural organizations. One of the most common problems in these sectors is the hazardous practice of farming. This type of farming operation makes vegetables very costly and unattractive. The reduction of costs is controlled by the need for labour. This issue is solved by adapting to new technology and compensating the labour deficiency. The algorithms that are processed in this method are Convolutional Neural Network (CNN), Object Detection and Generative Adversarial Network (GAN). These algorithms will enable the model to recognise faulty tomatoes. This model accurately identifies raw, ripe and defective tomatoes using deep learning methods and it is highly helpful for the betterment of agriculture. © 2023 IEEE.

关键词： Object detection

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 IEEE.

关键词： Reinforcement learning

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Efficient Protocols for the Secure Transmission of Training Parameters in a Federated Network: Research Frontiers, Taxonomy, and Future Directions

Efficient Protocols for the Secure Transmission of Training ...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Narendra Babu Pamula Ajoy Kumar Khan Arindam Sarkar Department of Computer Engineering Mizoram Central University Tanhril Aizawl Mizoram India Dept. of Computer Science & Electronics Ramakrishna Mission Vidyamandira Belur Math Howrah WB India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Federated Learning (FL), which ensures data privacy while jointly increasing model accuracy, has emerged as a viable method for collaborative machine learning across decentralized and heterogeneous data sources. However, because of differing computational capacities and security requirements, securely communicating training parameters across heterogeneous participants continues to be a significant difficulty. The present study offers an extensive overview of the state of research in the field and employs a taxonomy based on cryptography techniques, including Differential Privacy (DP), Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), and Blockchain technology, to classify the current secure transmission protocols. Additionally, it suggests future lines of inquiry for optimization and hybrid approaches to improve security, scalability, and efficiency in heterogeneous federated networks. For scholars and practitioners looking to propel the field of FL into more robust and privacy-preserving collaborative learning settings, the insights and taxonomy offered provide a road map

关键词： Training Technological innovation Protocols Federated learning Soft sensors Scalability Roads Taxonomy Propulsion Optimization

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The 2-MAXSAT Problem Can Be Solved in Polynomial Time

The 2-MAXSAT Problem Can Be Solved in Polynomial Time

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International Conference on Computational science and Computational Intelligence (CSCI)

作者： Yangjun Chen Dept. Applied Computer Science The University of Winnipeg Winnipeg MB Canada

By the MAXSAT problem, we are given a set $V$ of $m$ variables and a collection $C$ of $C$ clauses over $V$ . We will seek a truth assignment to maximize the number of satisfied clauses. This problem is NP-hard even for its restricted version, the 2-MAXSAT problem by which every clause contains at most 2 literals. In this paper, we discuss a polynomial time algorithm to solve this problem. Its time complexity is bounded by $\mathrm{O}(n^{2}m^{3})$ . Hence, we provide a proof of $P=NP$ .

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