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Leveraging AI to Personalize HR Marketing Campaigns: A Data-Driven Approach 3

Leveraging AI to Personalize HR Marketing Campaigns: A Data-...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Alshar, Moath Mahmoud Sao, Ameet Sharma, Manish Kadyan, Sunil Rao, Vuda Sreenivasa Anitha Vijayalakshmi, B. Jadara University Faculty of Business Business Administration Department Jordan National Institute of Construction Management and Research Haryana Bahadurgarh India Maharaja Agrasen Institute of Management Studies Department of Business Administration Delhi India Manav Rachna University School of Management and Commerce Faridabad India Koneru Lakshmaiah Education Foundation Department of CSE A.P. Vaddeswaram India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of ECE Chennai India

ISBN: (纸本)9798331523893

The research proposes an AI-driven personalization framework for enhancing the efficiency and engagement of HR marketing campaigns. The system integrates real-time data processing, collaborative filtering, and feedback loops to dynamically recommend job opportunities tailored to individual candidate preferences. Using an HR-related dataset from Kaggle, the framework preprocesses data by handling missing values, scaling numerical features via Min-Max Scaling, and reducing dimensionality using Principal Component Analysis (PCA). Collaborative Filtering is employed to predict candidate preferences through matrix factorization, dynamically updating recommendations based on real-time user interactions. The experimental evaluation highlights the framework's superior performance compared to traditional methods. Click-Through Rate (CTR) improved significantly, with AI-driven personalization achieving 30% compared to 18% from rule-based methods and 12% from the baseline. Accuracy analysis revealed that Collaborative Filtering outperformed random and content-based methods, achieving the highest precision (85%), recall (88%), and F1-score (86%). These results underscore the effectiveness of AI in aligning candidate preferences with recruitment goals while ensuring dynamic adaptation through feedback. The proposed system also seamlessly integrates personalized insights into HR marketing channels such as job portals, email campaigns, and social media advertisements, optimizing recruitment processes and enhancing user engagement. Future research aims to extend the dataset to include diverse candidate profiles, incorporate multi-modal data sources, and leverage deep learning for improved recommendation accuracy. Additionally, explainability techniques will be explored to ensure transparency and build trust among users. This framework demonstrates the transformative potential of AI-driven personalization in reshaping HR marketing strategies. © 2025 IEEE.

关键词： Convolutional Neural Network Cyber Security Multi-Layer Perceptron Network Anomaly detection Tuna Swarm Optimization

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The Vital Role of Small and Marginal Farmer in Future of Our Climate: Democratization of Machine learning, Artificial Intelligence, and Dairy Cow Necklace Sensors in Achieving the UN Climate Change Goals (COP21) and the Paris Agreement 25th

The Vital Role of Small and Marginal Farmer in Future of Our...

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25th international conference on Internet computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Vuppalapati, Chandrasekar Ilapakurti, Anitha Vissapragada, Sandhya Mamidi, Vanaja Vuppalapati, Sriya Vuppalapati, Akshay Kedari, Sharat Kedari, Santosh Vuppalapati, Jaya Kallakur, Karthik Rajasri, Koduru Lella, Narendra San Jose State University San Jose United States Hanumayamma Innovations and Technologies Inc. Fremont United States Hanumayamma Innovations and Technologiesa PVT Limited Hyderbad India

ISBN: (纸本)9783031859229

Small and marginal dairy farmers are a crucial part of the global farming community, and there are approximately 570 million farms worldwide, with the majority being small farms, as per the Food and Agriculture Organization (FAO). The international Farm Comparison Network (IFCN) estimates that there are around 133 million dairy farms globally. Despite being a significant source of income for small dairy farmers, they often face challenges regarding cattle productivity and health issues. Interestingly, cattle rumination counts could be the solution to these issues, not only for small farmers but also for the world at large. Cattle rumination count is a measure of how much time cows spend chewing cud, which is a mixture of partially digested food regurgitated from the cow’s stomach to the mouth and further broken down by chewing before being swallowed again. Rumination count is a critical indicator of a cow’s health and productivity since cows that chew more tend to have better digestion, absorb more nutrients from their feed, and produce more milk. Cattle rumination count is also related to methane emissions, as cows release methane gas during the digestive process, especially during rumination. Methane is a potent greenhouse gas that contributes to climate change. By using Dairy IoT Cattle Cow Necklace sensors, small and marginal farmers worldwide can track rumination counts proactively. The Dairy Cow Necklace sensor records the time spent chewing cud, and this data can be used by small farmers and veterinarians to identify cows experiencing digestive issues or other health problems and adjust their feeding and management practices accordingly. Reducing methane emissions from dairy cattle is a vital part of global efforts to mitigate climate change since livestock, including dairy cattle, contributes to around 14.5% of global greenhouse gas emissions. In a recent study, rumination counts were captured from three major dairy producing areas in India (Vishakhapatnam,

关键词： COP21 Cow Necklace Sensor Methan emissions Rumination

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PrimeK-Net: Multi-scale Spectral learning via Group Prime-Kernel Convolutional Neural Networks for Single Channel Speech Enhancement

PrimeK-Net: Multi-scale Spectral Learning via Group Prime-Ke...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zizhen Lin Junyu Wang Ruili Li Fei Shen Xi Xuan School of Electronic Information Sichuan University Chengdu China College of Intelligence and Computing Tianjin University Tianjin China School of Medicine Tohoku University Sendai Japan School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Linguistics and Translation City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Single-channel speech enhancement is a challenging ill-posed problem focused on estimating clean speech from degraded signals. Existing studies have demonstrated the competitive performance of combining convolutional neural networks (CNNs) with Transformers in speech enhancement tasks. However, existing frameworks have not sufficiently addressed computational efficiency and have overlooked the natural multi-scale distribution of the spectrum. Additionally, the potential of CNNs in speech enhancement has yet to be fully realized. To address these issues, this study proposes a Deep Separable Dilated Dense Block (DSDDB) and a Group Prime Kernel Feedforward Channel Attention (GPFCA) module. Specifically, the DSDDB introduces higher parameter and computational efficiency to the Encoder/Decoder of existing frameworks. The GPFCA module replaces the position of the Conformer, extracting deep temporal and frequency features of the spectrum with linear complexity. The GPFCA leverages the proposed Group Prime Kernel Feed-forward Network (GPFN) to integrate multi-granularity long-range, medium-range, and short-range receptive fields, while utilizing the properties of prime numbers to avoid periodic overlap effects. Experimental results demonstrate that PrimeK-Net, proposed in this study, achieves state-of-the-art (SOTA) performance on the VoiceBank+Demand dataset, reaching a PESQ score of 3.61 with only 1.41M parameters.

关键词： Time-frequency analysis Computational modeling Speech enhancement Signal processing Feature extraction Transformers Computational efficiency Complexity theory Convolutional neural networks Kernel

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Enhancing In Vitro Fertilization with Environment Optimization Utilizing Artificial Intelligence (EIVF-AI) 18th

Enhancing In Vitro Fertilization with Environment Optimizat...

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18th EAI international conference on Pervasive computing Technologies for Healthcare, PervasiveHealth 2024

作者： Khoshkangini, Reza Mangrio, Elisabeth Johnsson, Magnus Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Kristianstad University Kristianstad Sweden

ISBN: (纸本)9783031855740

In vitro fertilization (IVF) is of great aid to couples who are struggling to conceive. The IVF clinics, where couples undergo fertility treatments, require a carefully controlled environment to ensure the effectiveness of the procedures. In recent years, IVF has seen significant progress, thanks to new technologies and methods that improve success rates and expand options for infertile couples. One notable advancement involves combining pre-implantation genetic testing (PGT) with time-lapse imaging technology, which allows continuous monitoring of embryo development with minimal disturbance. This innovation improves the selection of healthy embryos for transfer, increasing success rates and reducing the risk of multiple pregnancies. However, maintaining a stable environment remains a key challenge. Fluctuations in temperature, humidity, air quality, and particulate matter can affect IVF success rates by disrupting the embryo’s delicate environment and potentially causing implantation failure. We discuss in this position paper our approach to alleviate such environmental problems in our project EIVF-AI funded by the Swedish funding agency Vinnova. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Transfer learning

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PointNet-Based 3D Point Cloud Target Classification Fused ISS3D and FPFH 8th

PointNet-Based 3D Point Cloud Target Classification Fused IS...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Gao, Zhicong Yi, Kaixiang Zhou, Wenju Chen, Yang Wang, Chao School of Mechatronical Engineering and Automation Shanghai Key Lab of Power Station Automation Technology Shanghai University Shanghai200072 China Shanghai Electric Automation Group CO. LTD Shanghai200023 China

ISBN: (纸本)9789819602933

In the field of point cloud classification using neural networks, the traditional PointNet neural network only focuses on single-point feature learning and ignores local information structure features, which makes the accuracy of point cloud classification low. In this paper, we propose IF-PointNet (Pointnet of ISS3D and FPFH) point cloud classification network to fill this gap. Firstly IF-PointNet adds the module of ISS3D point cloud key point detection on top of PointNet, so as to better learn the local point cloud structure features. Then, the module of FPFH feature extraction is added to encode the features of the detected key points, which strengthens the connection between the point cloud localities and improves the classification accuracy. The experimental results show that IF-PointNet has higher classification accuracy and robustness on the ModelNet40 dataset. The results verify the effectiveness of the two added modules in point cloud classification and brings new ideas and methods to the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： FPFH ISS3D Neural Network Point Cloud Classification PointNet

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Deep Feature Fusion of Local and Global Patterns for Early Detection of Lung Abnormalities in Chest X-Rays 4th

Deep Feature Fusion of Local and Global Patterns for Early D...

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Awasthi, Ashutosh Tiwari, Pawan Kumar Verma, Deepa Gupta, Akshansh Department of Computer Science and Engineering Institute of Engineering and Technology Uttar Pradesh Lucknow226021 India Faculty of Engineering and Technology Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh Lucknow226031 India CSIR-Central Electronics Engineering Research Institute Pilani India

ISBN: (纸本)9783031837951

Pneumonia and Tuberculosis are two major illnesses that pose significant threats to global health. Effective intervention and management of these disorders require an efficient early diagnostic system. In our work, we introduce a feature fusion network for categorizing Chest X-Rays into Pneumonia and Tuberculosis. Our feature fusion network combines the output features obtained from the local feature extractor and global feature extractor. It can help the model to identify both global perspectives and fine details during classification. Global level extraction module extracts broader features and patterns across the entire chest region. Also, by incorporating a local level extraction module, our fusion model can identify subtle variations within certain lung areas, which may be overlooked by the global level extractor due to its tendency to focus on broader patterns. Additionally, we integrated numerous small-sized datasets to develop an adequate dataset, which we used to evaluate the suggested strategy. Our suggested approach takes advantage of modern deep learning strategies and attains a tremendous accuracy of 98.56%, surpassing several pretrained architectures. It illustrates how effectively the model can identify different classes, thereby defining the effectiveness of the proposed technique in medical image classification tasks. Thus, the pro-posed method may aid medical professionals in accurately diagnosing Tuberculosis and Pneumonia using Chest X-Rays. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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KLFormer: Karhunen-Loève Transform for Robust 3D Human Pose Estimation

KLFormer: Karhunen-Loève Transform for Robust 3D Human Pose...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xin Zeng Haonan Luo Zihang Wang Sijia Li Leyu Zhang Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the scope of 3D human pose estimation, the task encompasses estimating the 3D positions of key skeletal points (i.e., wrists, elbows, and knees) from a 2D image or video sequence. This technology demonstrates widespread applicability across diverse domains, encompassing domains such as kinematic analysis, virtual reality, augmented reality, and medical imaging analysis. The common approach is divided into two stages: i) 2D Keypoint Detection: Detecting 2D keypoints from images. ii) 2D-to-3D Lifting: Converting 2D keypoints into 3D coordinates. Present research predominantly concentrates on Stage 2 and leverages sophisticated deep learning architectures, notably Transformers, yet two key challenges exist: i) suboptimal performance in predicting local details; ii) susceptibility to noise interference. In this work, we propose a time-principal component fusion model with limb segment property tracking is proposed to address the challenges above. By integrating timedomain features with principal components extracted through the Karhunen-Loève Transform (KLT), the model aims to address challenges related to feature extraction and noise reduction. Furthermore, to address the issue of suboptimal performance in predicting local details, we devise a property transformer to track the lengths of limb segments and predict the fixed property. Extensive experiments demonstrate that KLFormer showcases state-of-the-art performance on the standard benchmark dataset, Human3.6M.

关键词： Wrist Solid modeling Limbs Three-dimensional displays Pose estimation Noise Transforms Interference Transformers Feature extraction

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Detecting Alzheimer's Disease (Gender-Based) Using Different Machine learning Approaches

Detecting Alzheimer's Disease (Gender-Based) Using Different...

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international conference on Signal Processing and Communication (ICSC)

作者： Harendra Singh Negi Richa Indu Sushil Chandra Dimri Bhawnesh Kumar Nikhil Bisht Atika Gupta Department of Computer Science and Engineering Graphic Era deemed to be University Dehradun INDIA School of Computing Graphic Era Hill Univerity Dehradun India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

In the current scenario, Alzheimer's Disease constitutes 60-70% of neurodegenerative issues and affects 55 million people across the globe. As this disease progresses, the symptoms become worse and almost unmanageable. Thus, the early diagnosis of Alzheimer's Disease during the onset of mild symptoms, would be highly beneficial in tackling the challenges posed by this disease. Therefore, the work performs a comparative analysis (gender-based) of various methods including Support Vector Machine, k-Nearest Neighbours, Logistic Regression, Random Forest, Decision Trees, Naïve Bayes, and Modified k-Nearest Neighbours to diagnose Alzheimer's disease. For this, the Open Access Series of Imaging Studies (OASIS) dataset is used. The experimental analysis revealed that the Modified k-Nearest Neighbours is 99.12%, 99.49%, and 90.32% accurate in identifying Alzheimer's irrespective of gender, in male-gender, and female-gender subjects respectively. Additionally, other renowned classifiers such as Random Forest and cubic degree polynomial kernel Support Vector Machine are 98.96% accurate in detecting Alzheimer's irrespective of gender. The same classifiers are 97.62% accurate in diagnosing male Alzheimer's patients. Likewise, for distinguishing female Demented subjects from Non-demented cohorts, Random Forest and conventional k-Nearest Neighbours (at k=5) are 88.71% accurate. With such results emphasizing gender parameters, modified k-Nearest Neighbours is found more effective in diagnosing Alzheimer's Disease than other renowned classifiers.

关键词： Support vector machines Logistic regression Accuracy Open Access Signal processing Lead Polynomials Alzheimer's disease Random forests Monitoring

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Boosting Stock Predictions with Sentiment Analysis and Deep learning Models

Boosting Stock Predictions with Sentiment Analysis and Deep ...

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AI in Cybersecurity (ICAIC), international conference on

作者： Sarbagya Ratna Shakya Edgar Ceh-Varela Soneya Shakya Caleb Parten Zhaoxian Zhou Department of Mathematical Sciences Eastern New Mexico University Portales NM USA College of Business College of Fine Arts Eastern New Mexico University Portales NM USA Department of Mathematical Sciences Eastern New Mexico University Portales USA School of Computing Sciences and Computer Engineering University of Southern Mississippi Hattiesburg MS

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Since access to data has been on the rise and computational capabilities have improved, the prediction of stock prices has become very important. The work in this paper aims at integrating sentiment analysis and historical stock data to improve the accuracy of stock price forecasting. In our study, we have used Sentiment scores, which were derived from tweets and news using a zero-shot transformer-based model and combined with traditional stock metrics to predict future stock prices. To evaluate the accuracy of the models, three regression models based on recurrent neural networks: LSTM, BiLSTM and CNN+BiLSTM were compared with each other for different combination of input variables. Predictive accuracy was evaluated using statistical performance indicators including R-squared values and mean squared error. The results indicate that all the models could identify the trends of the stock movements. However, the BiLSTM model provided the best results and outperformed the other two models in terms of accuracy. The study demonstrated that incorporating sentiment scores significantly improves stock price prediction accuracy and offers a more comprehensive approach to stock prediction. Also, this study suggests the appropriate combination of input variables, such as sentiment and historical data, and the right model that can give the best stock price prediction. This work shows how qualitative sentiment data can be combined with quantitative stock metrics to enhance the predictive capability and may be helpful to investors and traders.

关键词： Measurement Sentiment analysis Analytical models Accuracy Input variables Bidirectional long short term memory Predictive models Transformers Data models Forecasting

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Towards a Framework for Promoting Student Engagement to Maximize learning in Higher Education: A Case Study 16th

Towards a Framework for Promoting Student Engagement to M...

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16th international conference on ICT Innovations, ICT Innovations 2024

作者： Nweke, Livinus Obiora El-Gazzar, Rania Noroff University College Tordenskjoldsgate 9 Kristiansand4612 Norway Borre Norway University of Agder Kristiansand Norway

ISBN: (纸本)9783031861611

Student engagement is a well-established factor that impacts the achievement of learning outcomes in higher education and as such, it is being widely researched. This paper explores the efficacy of the Context Challenge Activity Feedback (CCAF) framework in an in-person classroom setting for third-year bachelor students in an Information Systems (IS) Security course. Adopting a quantitative research methodology, the study analyzes student surveys to evaluate the effect of the CCAF framework on aspects such as the applicability of course material to real-world scenarios, course structure and clarity, stimulation of intellectual curiosity, and overall student satisfaction. The findings reveal that a majority of students perceive the course material as highly relevant to real-world IS Security issues, indicating the framework’s effectiveness in contextualizing theoretical concepts. The course’s structure and content clarity were also well-received, with a majority of students reporting positive experiences, suggesting the framework’s success in creating a clear and engaging learning environment. However, responses regarding the stimulation of intellectual curiosity showed variability, highlighting the necessity for adaptive teaching methods to cater to diverse learning preferences. Overall, the study reports high levels of student satisfaction, underscoring the potential of the CCAF framework in enhancing learning experiences in IS Security within traditional classroom settings. The study contributes to the existing studies by providing empirical support for the application and efficacy of the CCAF framework in in-person higher education settings, particularly in a technical and rapidly evolving field like IS Security. The findings have significant implications for pedagogical practices, suggesting that innovative, student-centered approaches like CCAF can effectively enhance student engagement and educational experiences in higher education. © The Author(s), under exc

关键词： Active learning

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