This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC mo...
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ISBN:
(数字)9798350378511
ISBN:
(纸本)9798350378528
This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC model can accurately differentiate between real and synthetic speech samples in various languages and its generalization capability across different linguistic contexts. Experimental evaluations show that the model achieves accuracies of 92% using Ensemble Learning (XGBoost and LightGBM), 93% with Meta Learning, and 90% with Neural Networks. Assessments encompass scenarios with real Arabic and English speech not included in training datasets, as well as different speakers across languages. The study contributes insights into the robustness and practical application of RVC for speech authentication.
Accurate text recognition and detection results for complex action-filled video images are very hard to achieve. To simplify the problem and enhance text detection and recognition capabilities, this work suggests Deep...
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ISBN:
(数字)9798350359299
ISBN:
(纸本)9798350359305
Accurate text recognition and detection results for complex action-filled video images are very hard to achieve. To simplify the problem and enhance text detection and recognition capabilities, this work suggests Deep Convolutional Encoder Model (DCEM) model for categorizing text from images with an action focus. This work builds the VGG16 network and ResNet50 to discover the properties of maximally stable extremal regions and acquires the general pixel-distribution level knowledge required to categorize action-oriented video. A multitask convolutional encoder network is utilized to acquire text components which are subsequently introduced to another network model. The method defines text-based classes by combining the outputs of the proposed DCEM model. To demonstrate the efficacy of the proposed strategy, experimentation trials were conducted on text dataset (10 classes of text information). Compared to previous work, the proposed method significantly improves specific text recognition.
Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommend...
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ISBN:
(数字)9798331505745
ISBN:
(纸本)9798331505752
Music recommendation systems have evolved from simple playlist curation techniques to sophisticated AI-driven models capable of analyzing human emotions for personalized song suggestions. Emotion-based music recommendation systems integrate advanced machine learning, deep learning, and artificial intelligence techniques to detect users' emotional states through various input modes, including facial expressions, speech tone, text analysis, and physiological signals. These systems aim to enhance user experience by dynamically selecting music that aligns with real-time emotions, thereby improving mood regulation and mental well-being. This review explores the methodologies employed in emotion-based music recommendation systems, including deep learning architectures, facial recognition techniques, and sentiment analysis models. It also discusses the challenges associated with real-time emotion detection, data privacy concerns, cross-cultural differences in music perception, and system adaptability. Furthermore, the paper highlights emerging trends and future research directions, such as multimodal emotion detection, hybrid AI models, reinforcement learning, and ethical AI frameworks. Addressing these challenges and advancements will be crucial in developing more robust, accurate, and user-centric emotion-based music recommendation systems.
Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an...
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Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.
Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial Intelligence, such as computer vision a...
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ISBN:
(数字)9798331505745
ISBN:
(纸本)9798331505752
Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial Intelligence, such as computer vision and Internet of Things, are remodeling waste management right into an automated and optimized procedure. This research study explores how AI-pushed technology enhance waste series, sorting, and recycling methods. Machine learning algorithms enhance waste category accuracy, computer vision permits unique fabric separation, and IoT-powered smart containers optimize collection schedules, decreasing needless trips and gasoline consumption. Additionally, AI-pushed direction optimization algorithms Significantly reduce emissions by means of designing efficient series pathways. These advancements Contribute. Not only to improve operational efficiency but also to the broader dreams of decreasing environmental impact and promoting a Circular economy. Despite its transformative potential, AI integration in waste control affords monetary, ethical, and technological challenges that require cautious attention. This examine provides a comprehensive evaluation of AI packages, their blessings, boundaries, and destiny capacity in revolutionizing waste management. With AI solutions, the enterprise can transition towards a more adaptive, efficient, and sustainable waste management ecosystem, addressing both urban and environmental demanding situations in the process.
Wireless communications are critical in the constantly changing environment of IoT and RFID technologies, where thousands of devices can be deployed across a wide range of scenarios. Whether connecting to cloud server...
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In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system ...
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ISBN:
(数字)9798350388107
ISBN:
(纸本)9798350388114
In the case of standalone houses, ensuring a continuous and regulated power supply from renewable sources is crucial. To address their unpredictable nature, an environmentally conscious hybrid renewable energy system combining photovoltaic panels, a battery, and a Diesel generator is considered. A software application simulates the power flow in a standalone house with common AC loads, analysing summer and winter scenarios over 24 hours. The strategy employed provides an optimal energy management by using the Diesel generator only during peak demand when PV is available and to cover loads and maintain battery life when PV is not in operation. The simulation results provide valuable insights for designing such hybrid energy systems and for enhancing system responsiveness to dynamic energy demands.
Wireless communications are critical in the constantly changing environment of IoT and RFID technologies, where thousands of devices can be deployed across a wide range of scenarios. Whether connecting to cloud server...
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ISBN:
(数字)9798350373592
ISBN:
(纸本)9798350373608
Wireless communications are critical in the constantly changing environment of IoT and RFID technologies, where thousands of devices can be deployed across a wide range of scenarios. Whether connecting to cloud servers or local fog/edge devices, maintaining seamless communications is difficult, especially in demanding contexts like industrial warehouses or remote rural areas. Opportunistic networks, when combined with edge devices, provide a possible solution to this challenge. These networks enable IoT devices, particularly mobile devices, to redirect information as it passes via other devices until it reaches an edge node. Using different communication protocols, this paper investigates their effects on response times and total messages received for a opportunistic assisted RFID system. Specifically, this article compares two communications technologies (Bluetooth 5 and Wize) when used for building a novel Opportunistic Edge Computing (OEC) identification system based on low-cost Single-Board computers (SBCs). For such a comparison, measurements have been performed for quantifying packet loss and latency. The tests consisted in two experiments under identical conditions and scenarios, with a node located roadside, transmitting identification information, and a node located inside a moving vehicle that was driven at varying vehicle speeds. The obtained results show for Bluetooth 5 average latencies ranging between 700 and 950 ms with packet losses between 7 % and 27 %, whereas for Wize the average delay as between 150 and 370 ms with packet losses between 20% and 52 %.
In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial ne...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
In order to facilitate massive connectivity and connecting the unconnected, aerial communications are becoming increasingly essential as a complement to terrestrial infrastructure. The integrated aerial-terrestrial network (IATN) offers both line-of-sight (LoS) and non-LoS (NLoS) connectivity and flexible deployment. This paper introduces a framework designed to optimize the cooperation between aerial and terrestrial networks, with the goal of maximizing the deployment cost efficiency (DCE) of the network (i.e., the ratio of the network's total data transmission rate to the combined deployment and energy costs). The cooperation among unmanned aerial vehicles (UAVs) and terrestrial base-station (BSs) is supported with clustered cell-free massive MIMO (C-CF-M-MIMO). Specifically, we formulate a problem focused on maximizing the DCE while adhering to power constraints and zero intra-cell pilot contamination. Subsequently, we propose a pilot-contamination aware user clustering, and a distributed coalition formation game for BSs and UAVs clustering in C-CF-M-MIMO-enabled IATN. Our numerical findings demonstrate the efficacy of the proposed algorithm when compared to conventional benchmark methods. Furthermore, the C-CF-M-MIMO-enabled IATN outperforms BSs-only and UAVs-only network equipped with typical cell-free configurations, such as (i) traditional CF-MIMO and (ii) user-centric CF-MIMO.
Othello is a two-player combinatorial game with 1E+28 legal positions and 1E+58 game tree complexity. We propose a HIghly PArallel, Scalable and configurable hardware accelerator for evaluating the middle and endgame ...
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