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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Enhancing Efficiency in Livestock Monitoring and Management with IoT Solutions '

Enhancing Efficiency in Livestock Monitoring and Management ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sahoo, Girija Shankar Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This technical abstract makes a specialty of an Iota-based strategy to enhance the performance of livestock monitoring and management. The proposed answer involves the usage of wireless sensors and mobile gadgets to gather records regarding the environment and the health of the animals, which are then processed and transmitted to the cloud for similar analysis. The evaluation from the cloud can then be used to detect modifications in the livestock's health or surroundings, trigger indicators, and inform endorsed moves. This solution reduces the time requirements for facts collection and evaluation and consequently decreases the ability for diseases to occur or unfold many of the farm animals. Moreover, the solution can offer vital statistics for farmers and ranchers to make higher selections to enhance the productivity of their herds and streamline operational processes. © 2024 IEEE.

关键词： management modifications requirements surroundings technical abstract

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Sustainable Water Futures: Enhancing Efficiency And Conservation In Madinah AL Munawara

Journal of Applied Science and Engineering

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Journal of Applied Science and engineering 2024年第10期28卷 2027-2042页

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Nawaz, Waqas Namoun, Abdallah Muhammad, Munir Azam Faculty of Computer and Information System Islamic University of Madinah Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Madinah42351 Saudi Arabia AI Center Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia Khwaja Fareed University of Engineering & Information Technology Faculty of Computer and Information System Punjab Rahim Yar Khan Pakistan

Water resource management worldwide faces significant challenges, including high consumption rates, scarcity, and ageing infrastructure. This paper proposes a comprehensive approach to address these issues through five key solutions. Advanced Metering Infrastructure (AMI) first facilitates real-time data collection and consumer awareness campaigns. Secondly, wastewater recycling and reuse are promoted by upgrading wastewater treatment plants and fostering public-private partnerships. Thirdly, reducing network losses is achieved through leak detection systems and regular maintenance. Fourthly, policy enhancement and regulatory frameworks are strengthened by implementing regulations for new developments and incentives for water-saving technologies. Lastly, community engagement and education initiatives are undertaken to promote sustainable water practices. This multifaceted strategy aims to improve water conservation, efficiency, and resilience of the water supply system. This paper proposes solutions that can be applied to the Madinah AL Munawara region of Saudi Arabia, an area of utmost importance, demonstrating the applicability and effectiveness of these solutions in addressing regional water management challenges. © The Author(’s).

关键词： Wastewater treatment

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Heart rate variability analysis in controls and epilepsy patients with or without receiving treatment: a clinical review and meta-analysis

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Neural Computing and Applications 2024年第29期36卷 18043-18063页

作者： Shahnawaz, Muhammad Bilal Dawooda, Hassan Iqbal, Uzair Department of Information Engineering Technology National Skills University Islamabad Islamabad4400 Pakistan Software Engineering Department University of Engineering and Technology Taxila Pakistan Department of Artificial Intelligence and Data Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The malfunctioning of cardiac autonomic control in epileptic patients develops ventricular tachyarrhythmia and causes sudden unexpected death in epilepsy patients (SUDEP). Various clinical studies investigated the effect of epilepsy on cardiac autonomic control by performing heart rate variability (HRV) analysis;however, results are unclear regarding whether sympathetic, parasympathetic, or both branches of the autonomic nervous system (ANS) are affected in epilepsy and also the impact of anticonvulsant treatment on the ANS. This study follows the systematic protocols to investigate epilepsy and its anticonvulsant treatment on cardiac autonomic control by using linear and nonlinear HRV analysis measures. The electronic databases of PubMed, Embase, and Cochrane Library were used for the collection of studies. Initially, 1475 articles were identified whereas after 2-staged exclusion criteria, 33 studies were selected for execution of the review process and meta-analysis. For meta-analysis, four comparisons were performed (epilepsy patients): (1) controls (healthy subject with no history of epilepsy) versus untreated patients;(2) treated (patients under treatment that have a seizure) versus untreated patients;(3) controls versus treated patients;and (4) refractory versus well-controlled (epilepsy patients that were seizure-free for last 1 year). For treated and untreated patients, there was no significant difference whereas well-controlled patients presented higher values as compared to refractory patients. Meta-analysis was performed for the time-domain, frequency-domain, and nonlinear parameters. Untreated patients in comparison with controls presented significantly lower HF (high-frequency) and LF (low-frequency) values. These LF (g = − 0.9;95% CI − 1.48 to − 0.37) and HF (g = − 0.69;95% confidence interval (CI) − 1.24 to − 0.16) values were affirming suppressed both, vagal and sympathetic activity, respectively. Additionally, LF and HF value was increased in most o

关键词： Heart

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Energy-efficient virtual machine scheduling in IaaS cloud environment using energy-aware green-particle swarm optimization

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International Journal of information technology (Singapore) 2023年第4期15卷 1927-1935页

作者： Ajmera, Kashav Tewari, Tribhuwan Kumar Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida-62 Noida India

Cloud data centers have a huge pool of heterogeneous servers to seamlessly meet the ever-increasing demand for cloud resources. Most of these servers belong to different server generations and have varying power ratings. Virtual machine (VM) consolidation schedules VMs over this pool of heterogeneous servers to improve resource utilization and reduce power consumption. However, it also increases the Service Level Agreement(SLA) violation since servers operate near full capacity. Therefore, to optimize this tradeoff between energy consumption and SLA violation, a Green-Particle Swarm Optimization (GPSO) algorithm is proposed that schedules VMs over energy-efficient green servers. These green servers (green particles) are energy-efficient, explore the global search space, and find a VM schedule with minimum active servers that reduce data center power consumption and ensure SLA. The proposed GPSO algorithm is implemented on the cloudsim simulator and compared with the state-of-the-art VM scheduling algorithms. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Bin packing Cloud computing Cloud data center Energy-efficient VM scheduling Green computing Particle swarm ptimization Power consumption Virtualization

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A novel computer-Aided Estimation of Breast Cancer via Time Series Analysis

A novel Computer-Aided Estimation of Breast Cancer via Time ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bhambu, Pawan Haripriya Yadav, Dhyan Chandra Vivekananda Global University Department of Computer Science and Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Introduces a estimation based totally on time series evaluation. among ladies, and it's far more critical to stumble on and deal with it in the early ranges. However, due to time consumption within the analysis method and its cost, there's a need to increase pc-aided structures to automate the analysis. It proposes a time series evaluation method based on machine mastering for detecting and estimating breast most cancers degree (early or past due) at the preliminary ranges of prognosis. The proposed device is predicated on the type of records amassed in an early stage of breast most cancers analysis, along with age, tumor size, lymph node involvement, and other laboratory values, which includes estrogen and progesterone receptor degrees. The proposed machine changed into tested using a dataset of patients with breast cancers. The device's performance was evaluated using the region under the curve and F1 rating. a unique method estimating possibility of diagnosis through time series evaluation. The proposed method applies a sparse illustration model for pre-processing, observed via the utility of a random wooded area classifier to evaluate the chance of breast cancer. public dataset of 961 female sufferers with breast cancer from the UCI machine studying repository. The proposed approach is faster and more correct than the associated methods. The concept of using time collection evaluation for estimating breast cancers is, for this reason promising as it could be deployed in medical exercise to offer reliable detection of breast cancer. © 2024 IEEE.

关键词： Time series analysis

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Learning Salient Features for Speech Emotion Recognition using Attention-based Residual Bidirectional LSTM with Federated Learning 4

Learning Salient Features for Speech Emotion Recognition usi...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Lucknow Campus Amity School of Engineering and Technology Department of Computer Science and Engineering India Indian Institute of Information Technology Allahabad Department of Information Technology India

ISBN: (纸本)9798350343670

In human communication, expressing human emotion plays an essential role in transferring information to the other individual. The expression forms of human communication are very rich, in different patterns like facial expressions, voice tone, eye contact, body language and laughter. Moreover, the languages used by individuals in the entire world are different, yet without understanding the communication language, the individuals can realize the message that other individuals require to convey as emotional expressions. Among all the human emotional expressions, the conveyed expressions through human voice are experimented with in different studies. However, identifying the emotional situation of the presenter is complicated for the machine learning techniques, which gained more attraction in Speech Emotion Recognition (SER). Here, the SER framework plays an efficient role in different real-world applications in observing human actions, virtual reality, emergency centers and robot communication to observe the emotional phase of the speakers. Thus, an innovative federated learning-based SER is presented in this paper. The essential speech signals are initially collected from the standard database. After that, the collected signal is fed to the feature extraction stage. Here, the deep features in the input signal are obtained by the Variational Autoencoder (VAE). Further, the deep features obtained from the VAE are passed to the "Attention-based Residual Bidirectional Long Short-Term Memory (A-RBiLSTM)"to identify the emotions in the speech signal. At last, different experimental analyses are executed to observe the effectualness of the implemented framework. © 2024 IEEE.

关键词： Attention-based Residual Bidirectional Long Short-Term Memory Deep Features Feature Extraction Federated Learning Speech Emotion Recognition Variational Autoencoder

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Understanding Time Series Analysis in Early Detection of Unclassified Endocrine Tumors

Understanding Time Series Analysis in Early Detection of Unc...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Seth, Jyoti Prakash, Febin Singh, Vaishali Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Endocrine tumors are malignant tumors that get up inside the endocrine system, a network of glands and organs responsible for producing hormones that affect the physiological procedures of the frame. Although endocrine tumors are usually unusual, early detection is critical for successful treatment. Due to the lack of reliable medical markers for endocrine tumors, early detection is mainly predicated on imaging and laboratory tests. However, these checks may be luxurious and can be hard to interpret. In recent years, time collection analysis (TSA) has been gaining popularity as a powerful device for the early detection of unclassified endocrine tumors. Time series analysis is a form of statistical evaluation that applies mathematical fashions to datasets by converting data values over a hard and fast time frame. It's miles used to discover trends in the facts, making it possible to discover abnormal behavior that could indicate contamination. This method has been shown to have a high degree of accuracy. It may provide insight into, in any other case, unclassified endocrine tumors, making an allowance for the set-off detection and remedy. In this paper, we discuss the capacity of time collection evaluation inside the early detection of unclassified endocrine tumors, alongside the demanding situations and opportunities associated with its use. Time series evaluation has been established to be a powerful tool for the early detection of unclassified endocrine tumors. By presenting perception into patterns and developments in temporal information, this method allows studies to pinpoint regions of molecular change from which ability biomarkers can be diagnosed. The technique is predicated on measuring and analyzing adjustments inside the gene expression levels of regulation networks over time. This method has been used to become aware of adjustments within the expression of pathways related to G protein-coupled receptors and pathways associated with endocrinology. Moreove

关键词： detection Endocrine illnesses improvement reliable

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Urdu Sentiment Analysis Using Deep Learning 18

Urdu Sentiment Analysis Using Deep Learning

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Nasir, Imtisall Bazai, Sibghat Ullah Ghafoor, Muhammad Imran Marjan, Shah Balochistan University of Information Technology Engineering and Management Science Department of Computer Science Quetta Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Computer Engineering Quetta Pakistan Pakistan Television Corporation Department of Engineering Lahore Pakistan Balochistan University of Information Technology Engineering and Management Science Department of Software Engineering Quetta Pakistan

ISBN: (纸本)9798331508692

Sentiment Analysis is a deep text analysis to determine the expressions from the written text, The Urdu language holds significant importance due to the usage of these social media platforms and content generated at these platform in Urdu. Effective sentiment analysis in Urdu can provide valuable information about social trends, customer feedback, and public opinion on different sets of topics in the Urdu context. This study uses an deep learning (ANN) Artificial Neural Network to predict and classify the sentiments in the Urdu language, labeling them into positive, negative, and neutral sentiments. To achieve the research goals, advanced preprocessing techniques were utilized such as removing stop words, emojis, emails, and characters, removing symbols, performing lemmatization to expand the meaning of text, tokenized each word by using Lexicon-based approach and ngram. The dataset are tweets dependent and have 1,140,021 tweets. These steps ensure that data is standardized and cleaned to be split into training and testing. The datasets were converted into sequence and categorized to be input into the ANN model. The ANN model was trained and achieved an accuracy of 90%, precision of 98%, and f1-score of 97%. These high values indicate that the model accurately predicted the sentiments in the Urdu text. However, during experimentation limitations were discovered due to the complexities of Urdu text, difficult script patterns, and linguistic problems that were not solved by the preprocessing technique. During analysis, the limitation was discovered due to the limited number of preprocessing techniques and tools for Urdu text. However, the study highlights the potential of neural networks, especially ANN in Urdu sentiment analysis. This study contributed significantly to Urdu natural language processing by providing robust techniques and identifying limitations to work on it as a research problem. © 2024 IEEE.

关键词： Natural language processing systems

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Application of a three-phase unified power quality conditioner in a microgrid

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IET Smart Grid 2024年第6期7卷 872-890页

作者： Wu, Tsung-Hsun Huang, Shu-Syuan Chen, Pei-Yin Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Power Electronics Industrial Technology Research Institute Tainan Taiwan

In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the power system. As an advanced power electronics device, the unified power quality conditioner (UPQC) can effectively address these problems. Therefore, the integration of UPQC into power distribution lines can enhance the stability and reliability of the power system, providing higher-quality power supply to meet the needs of both users and suppliers for PQ. The authors utilise the TI TMS320F28377D chip as the control core to construct a simple yet highly efficient UPQC system. Through simulation, the correctness of the voltage and current compensation algorithms has been verified, and the overall system feasibility has been validated using a 100-kVA series-shunt inverter. The experimental results show that the voltage compensation range of the UPQC is 380V ± 10%. During sudden current drops, the imbalance rate can be reduced from 85.5% to 1.32% after controlled compensation. During sudden current rises, the imbalance rate can be reduced from 13.1% to 1.56% after controlled compensation. These findings demonstrate the effectiveness of the UPQC system in improving PQ and system stability in power distribution networks. © 2024 The Author(s). IET Smart Grid published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Power quality

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