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Information Technology Research and Innovation (ICITRI), International Conference on

作者： Erick Fernando Yulius Denny Prabowo Winanti Department of Information Systems Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia Department of Information Systems Universitas Insan Pembangunan Indonesia

ISBN: (数字)9798350376210

ISBN: (纸本)9798350376227

In the current digital era, marketing has developed using technology through media to reach consumers. One prominent feature is influencer marketing, where individuals with significant social media influence promote products or services to their audiences. This strategy has become dominant because of its potential to expand brand reach and boost sales. However, there are challenges in ensuring the effectiveness of influencer marketing, particularly in delivering content with sufficient information and entertainment value. Overcoming these challenges is critical to maximizing customer purchase intent. This research explores the influence of information value, entertainment value, brand awareness, and customer trust on customer purchase intentions in influencer marketing. This research used an online survey (google form) with 398 valid respondents used for analysis PLS-SEM with SMART PLS 3.0. The indicator test results are valid and reliable with loading value for each item with a value >0.5, AVE value $\geqslant 0.50$, Cronbach’s alpha value $\geqslant 0.70$ and Composite Reliability (CR) value $\geqslant 0.7$. The findings show that these factors have a significant effect on purchase intention. In emphasizing the importance of influencers delivering educational and entertaining content to increase Brand Awareness in building customer trust, with the consistency of influencers. Overall, providing valuable insights for companies looking to optimize their influencer marketing strategy to increase brand awareness and customer trust, ultimately driving customer purchase intent.

关键词： Surveys Technological innovation Social networking (online) Buildings Entertainment industry Psychology Companies Data models Reliability Testing

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Experiments and modeling on thermal performance evaluation of standalone and M-cycle based desiccant air-conditioning systems

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Energy Reports 2024年 11卷 1445-1454页

作者： Bhat, Showkat Ahmad Sajjad, Uzair Hussain, Imtiyaz Yan, Wei-Mon Raza, Hafiz M.U. Ali, Hafiz Muhammad Sultan, Muhammad Omar, Hani Azam, Muhammad Waheed Bozzoli, Fabio Huang, Nen-Fu Center of Innovative Incubator National Tsing Hua University Hsinchu300044 Taiwan Department of Computer Science National Tsing Hua University Hsinchu300044 Taiwan Department of Energy and Refrigerating Air-Conditioning Engineering National Taipei University of Technology Taipei10608 Taiwan Research Center of Energy Conservation for New Generation of Residential Commercial and Industrial Sectors National Taipei University of Technology Taipei10608 Taiwan Department of Agricultural Engineering Bahauddin Zakariya University Bosan Road Multan60800 Pakistan Dhahran31261 Saudi Arabia Faculty of Information Technology Zarqa University Zarqa Jordan Department of Architecture and Engineering University of Parma Parco Area delle Science 181/A Parma43124 Italy IRC-Renewable Energy and Power Systems King Fahd University of Petroleum and Minerals Dammam Dhahran Saudi Arabia

This study aims to evaluate the performance of standalone and Maisotsenko-cycle based desiccant air-conditioning systems with silica gel through experimental and explainable artificial intelligence methods. In order to examine the desiccant systems' performance, deep neural networks have been constructed based on the optimal hyper-parameters provided by the Bayesian surrogacy. The influential factors, such as regeneration air temperature and relative humidity, primary air temperature and relative humidity, among other system features like regeneration temperature and humidity, are chosen as the inputs using feature engineering and correlation heat maps. The results indicated that with the right framework and training data, the proposed model can forecast the performance of the standalone and M-cycle-based desiccant air conditioning systems with a great accuracy (i.e. R2 = 0.99). In addition, regeneration temperature and humidity, and inlet regeneration air temperature are considered to be the primary parameters that affect the thermal performance of Maisotsenko-cycle and stand-alone desiccant systems. The regeneration conditions are shown to have a positive correlation with the primary and regeneration outlet temperatures while having a negative correlation with the primary and regeneration outlet relative humidity. The primary inlet relative humidity has the greatest impact on the primary outlet relative humidity while the regeneration inlet temperature has the highest impact on the regeneration outlet relative humidity of the system. In addition, the primary inlet relative humidity is found to have positive correlations with the outlet regeneration and primary relative humidity and outlet regeneration temperature. Also, the primary air inlet temperature is found to be directly associated with the outlet primary relative humidity and temperature for the considered systems. © 2024 The Authors

关键词： Air conditioning

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A Comparison of Different Optimization Techniques in AlexNet for Plant Disease Classification: SGD, Adadelta, ASGD and Adagrad

A Comparison of Different Optimization Techniques in AlexNet...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Ponnila P Bazila Banu A Professor Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India Department of Computer Science and Business Systems KPR Institute of Engineering and Technology Coimbatore Tamilnadu India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Plant diseases are a biggest challenge for the farmers. Agriculture relies heavily on its production, and diagnosis of plant disease in an accurate manner will help in identification of the appropriate disease. If correct identification was not performed, there might be a significant financial loss that could have an impact on both farmers and the national economy. For resolving this issue, the AlexNet was employed, a convolutional neural network method. In this research, AlexNet is used to classify the diseases occurring in chilli, cauliflower and sunflower. This AlexNet model, with Stochastic Gradient Descent(SGD) algorithm optimization techniques had achieved 82% accuracy for the overall classification of the test images of chilli plant, 98% accuracy for the overall classification of the test images of cauliflower plant and 91% accuracy for the overall classification of the test images of sunflower plant; with Adadelta optimization 86%, 99% and 93% accuracy was achieved for the overall classification of the test pictures of chilli plant, cauliflower plant and sunflower plant respectively. After using the Asynchronous Stochastic Gradient(ASGD) optimization with AlexNet model, the accuracy obtained are 88% for the test images of chilli plant, 98% for the test images of cauliflower plant and 91% for the test images of sunflower plant; and finally, AdaGrad optimization with AlexNet achieved accuracy of 80% for the test images of chilli plant, 9 8% for the test images of cauliflower plant and 93% for the test images of sunflower plant.

关键词： Training Plant diseases Visualization Accuracy Image resolution Stochastic processes Production Feature extraction Robustness Optimization

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Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach

arXiv

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arXiv 2024年

作者： Hakim, Safayat Bin Adil, Muhammad Acharya, Kamal Song, Houbing Herbert Department of Information Systems University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States

The escalating sophistication of Android malware poses significant challenges to traditional detection methods, necessitating innovative approaches that can efficiently identify and classify threats with high precision. This paper introduces a novel framework that synergistically integrates an attention-enhanced Multi-Layer Perceptron (MLP) with a Support Vector Machine (SVM) to make Android malware detection and classification more effective. By carefully analyzing a mere 47 features out of over 9,760 available in the comprehensive CCCS-CIC-AndMal-2020 dataset, our MLP-SVM model achieves an impressive accuracy over 99% in identifying malicious applications. The MLP, enhanced with an attention mechanism, focuses on the most discriminative features and further reduces the 47 features to only 14 components using Linear Discriminant Analysis (LDA). Despite this significant reduction in dimensionality, the SVM component, equipped with an RBF kernel, excels in mapping these components to a high-dimensional space, facilitating precise classification of malware into their respective families. Rigorous evaluations, encompassing accuracy, precision, recall, and F1-score metrics, confirm the superiority of our approach compared to existing state-of-the-art techniques. The proposed framework not only significantly reduces the computational complexity by leveraging a compact feature set but also exhibits resilience against the evolving Android malware landscape. © 2024, CC BY-NC-SA.

关键词： Android malware

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1-Bit Reconfigurable Transmitted/Reflected Array (TRA) for 5G/6G Wireless Communication

1-Bit Reconfigurable Transmitted/Reflected Array (TRA) for 5...

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European Conference on Antennas and Propagation, EuCAP

作者： Yujie Liu James R. Kelly Mark Holm Srikanth Gopal Sina Rezaei Aghdam Yuanwei Liu Electronic Engineering and Computer Science Queen Mary University of London London UK Radio Base-station Systems Department Huawei Technologies (Sweden) AB Gothenburg Sweden

This paper introduces a design for a 1-bit reconfigurable Transmitted/Reflected Array (TRA), capable of electronic beamforming in both transmission and reflection. The TRA is constructed using the Unit Cells (UCs) that respond differently to TE and TM incidences. By employing the PIN diodes, the UC achieves two phase states, 0° and 180°, for the transmission and reflection, respectively. Simulation results show that the proposed TRA has demonstrated independent control of the transmitted and reflected beams at the working frequency of 5.9 GHz. A peak gain of 18.1 dBi and a scanning range of 0° to 60° have been achieved in both transmission and reflection, making it suitable for applications in 5G/6G wireless communication systems.

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FetchAid: Making Parcel Lockers More Accessible to Blind and Low Vision People With Deep-learning Enhanced Touchscreen Guidance, Error-Recovery Mechanism, and AR-based Search Support

arXiv

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arXiv 2024年

作者： Guan, Zhitong Xiong, Zeyu Fan, Mingming Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou China Division of Integrative Systems and Design Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Parcel lockers have become an increasingly prevalent last-mile delivery method. Yet, a recent study revealed its accessibility challenges to blind and low-vision people (BLV). Informed by the study, we designed FetchAid, a standalone intelligent mobile app assisting BLV in using a parcel locker in real-time by integrating computer vision and augmented reality (AR) technologies. FetchAid first uses a deep network to detect the user's fingertip and relevant buttons on the touch screen of the parcel locker to guide the user to reveal and scan the QR code to open the target compartment door and then guide the user to reach the door safely with AR-based context-aware audio feedback. Moreover, FetchAid provides an error-recovery mechanism and real-time feedback to keep the user on track. We show that FetchAid substantially improved task accomplishment and efficiency, and reduced frustration and overall effort in a study with 12 BLV participants, regardless of their vision conditions and previous experience. © 2024, CC BY-NC-ND.

关键词： Augmented reality

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Hessian Fly Infestation Assessment with Stress Indicator based on Multispectral Imaging and Machine Learning Techniques

Hessian Fly Infestation Assessment with Stress Indicator bas...

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2024 American Society of Agricultural and Biological Engineers Annual International Meeting, ASABE 2024

作者： Kwaku, Opoku-Ware Steven, Odubiyi Sanford, Eigenbrode Li, Liujun Department of Soil and Water Systems University of Idaho 875 Perimeter Drive MS 2060 MoscowID83844-2060 United States Department of Entomology Plant Pathology & Nematology 875 Perimeter Drive MS 2060 MoscowID83844-2060 United States Department of Mechanical Engineering University of Idaho MoscowID United States Department of Computer Science University of Idaho MoscowID United States

ISBN: (纸本)9798331302214

Remote sensing technologies and vegetation indices play a crucial role in the early detection and monitoring of biotic stresses, such as Hessian fly infestation in wheat plants. By analyzing and comparing the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) in Hessian fly infestation treatment and control plants, we aimed to evaluate these indices as indicators of plant health. The study involved the use of multispectral images to calculate NDVI and NDRE in selected regions of interest. The results showed that on the 14th day after infestation, NDVI for infected plants ranged from 0.2947 to 0.4577, while NDVI for non-infected plants ranged from 0.3464 to 0.4689. On the 21st day, both NDVI and NDRE declined, with a more significant drop in infected plants. A K-nearest neighbor (KNN) classifier machine learning model was developed to categorize plant health status based on NDVI and NDRE values, achieving 80% and 70% accuracy, respectively. A Support Vector Machine (SVM) model with polynomial and radial basis function (RBF) kernels was also applied, achieving accuracies up to 90%. The study demonstrated that the pronounced reduction in NDVI and NDRE in infected plants indicates deteriorating canopy greenness and chlorophyll content, suggesting worsening plant health. The accuracy of the KNN and SVM classifiers highlight the potential of combining remote sensing with machine learning for monitoring and assessing plant diseases. Overall, the developed multispectral imaging and machine learning models provides an effective means of identifying the progression of Hessian fly infestation over time and offers a framework for assessing plant health and classify disease occurrence/severity, enabling prompt responses decision making for precision farming. © 2024 ASABE Annual International Meeting. All rights reserved.

关键词： Nearest neighbor search

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Multimodal Classification and Out-of-distribution Detection for Multimodal Intent Understanding

arXiv

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arXiv 2024年

作者： Zhang, Hanlei Zhou, Qianrui Xu, Hua Su, Jianhua Evans, Roberto Gao, Kai State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

Multimodal intent understanding is a significant research area that requires effectively leveraging multiple modalities to analyze human language. Existing methods face two main challenges in this domain. Firstly, they have limitations in capturing nuanced and high-level semantics underlying complex in-distribution (ID) multimodal intents. Secondly, they exhibit poor generalization when confronted with unseen out-of-distribution (OOD) data in real-world scenarios. To address these issues, we propose a novel method for both ID classification and OOD detection (MIntOOD). We first introduce a weighted feature fusion network that models multimodal representations effectively. This network dynamically learns the importance of each modality, adapting to multimodal contexts. To develop discriminative representations that are conducive to both tasks, we synthesize pseudo-OOD data from convex combinations of ID data and engage in multimodal representation learning from both coarse-grained and fine-grained perspectives. The coarse-grained perspective focuses on distinguishing between ID and OOD binary classes, while the fine-grained perspective enhances the understanding of ID data by incorporating binary confidence scores. These scores help to gauge the difficulty of each sample, improving the classification of different ID classes. Additionally, the fine-grained perspective captures instance-level interactions between ID and OOD samples, promoting proximity among similar instances and separation from dissimilar ones. We establish baselines for three multimodal intent datasets and build an OOD benchmark. Extensive experiments on these datasets demonstrate that our method significantly improves OOD detection performance with a 3-10% increase in AUROC scores while achieving new state-of-the-art results in ID classification. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Correction to: Convolutional neural network and ensemble machine learning model for optimizing performance of emotion recognition in wild (Multimedia Tools and Applications, (2023), 10.1007/s11042-023-17744-1)

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Multimedia Tools and Applications 2024年 1-1页

作者： Alturki, Nazik Umer, Muhammad Alshardan, Amal Saidani, Oumaima Abate, Andrea F. Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Department of Computer Science University of Salerno Fisciano84084 Italy Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

The authors Muhammad Umer and Oumaima Saidani have two affiliations in the original publication of this article which was incorrect. The original article has been corrected. © 2024, The Author(s), under exclusive...

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A Machine Learning Method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Preetha, V. Sathishkumar, P. Janani, M. Manohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector Machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well. © 202

关键词： Sentiment analysis

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