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Emotion mining for early suicidal threat detection on both social media and suicide notes using context dynamic masking-based transformer with deep learning

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Multimedia Tools and Applications 2025年第13期84卷 11729-11752页

作者： Kodati, Dheeraj Tene, Ramakrishnudu Department of Computer Science and Engineering National Institute of Technology Warangal India

Suicide represents a poignant societal issue deeply entwined with mental well-being. While existing research primarily focuses on identifying suicide-related texts, there is a gap in the advanced detection of mental health states leading to suicidal tendencies. The purpose is to recognize the emotions influencing individuals’ mental states, potentially leading to suicidal behavior. This study seeks to identify specific emotions like anger, depression, anxiety, guilt, fear, stress, and sadness within suicide-related texts sourced from both social media and suicide notes. We introduce a novel suicide severity assessment method emphasizing emotion detection across categories like suicidal ideation, suicide planning, attempted, committed, suicide notes, describing, and cannot dare. By identifying high-intensity negative emotions, our approach enables early detection of potential suicide threats. Existing studies often struggle to capture nuances and semantic relationships, particularly in suicide-related contexts. To address this, we propose a context dynamic masking based on bidirectional long short-term memory along with a multi-head self-attention and convolutional neural network (CoDyn-BMHSA-CNN) model, capturing bidirectional contexts and maintaining variable-length sequences. Our model achieves accuracies of 98.2% and 97.4%, with F1 scores of 95.5% and 93.8% for social media and suicide note datasets, respectively. We perform ablation tests to pinpoint the key components of our proposed models. Furthermore, a thorough comparative analysis evaluates the model’s performance across various contexts and platforms. Experimental results illustrate the superior effectiveness of our model in identifying emotions from text sequences compared to state-of-the-art approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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An efficient secure channel establishment through lightweight key distribution in e-health communication systems

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Multimedia Tools and Applications 2025年第11期84卷 9111-9131页

作者： Ebrahimi, Iman Nikooghadam, Morteza Department of Computer Engineering and Information Technology Imam Reza International University Mashhad Iran

E-healthcare has significantly improved healthcare services and overall health by utilizing digital technologies such as the internet, computers, and mobile devices. However, secure communication channels play a critical role in ensuring the safety and confidentiality of patients' information. Consequently, several user authentication and key agreement protocols have been developed to address this issue. Although numerous proposed protocols are robust against security attacks, researchers have reported vulnerabilities. In this study, we evaluate one such protocol and identify vulnerabilities to insider attacks, server impersonation attacks, and replay attacks. To address these vulnerabilities, we propose a two-factor mutual authentication and key agreement scheme that offers a secure, efficient, and effective solution with low computation cost, enhanced security, and superior performance against diverse security attacks. Our proposed technique guarantees the integrity, confidentiality, and availability of patient information while ensuring the authenticity of users during communication. We conducted thorough comparisons focusing on the practical implementation of ARM microcontrollers to assess computation efficiency. The total estimated execution time of our proposed scheme for registration and authentication phases is approximately 975.8 µs, which is the shortest among all related works. This finding illustrates that our proposed scheme is well-suited for applications in e-health, telemedicine, and healthcare systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Desktop Voice Assistant for Elderly People using Feed-forward Neural Network for Intent Recognition 2024

Desktop Voice Assistant for Elderly People using Feed-forwar...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Garg, Kashish Alam, Taj Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

technology has deeply embedded itself in our daily lives, making it easier to carry out tasks. However, the use of technology can be complicated for some like elderly people, cognitively and visually impaired individuals, illiterate, or people with limited education. A proposed desktop voice assistant for elderly people using a feed-forward neural network for intent recognition to simplify complex tasks and make technology more accessible for those who find it difficult. The assistant is a companion to assist, automate, and make it easier for older people to use computers. It aids them in understanding, responding to user needs, and assisting with task management and data retrieval. The Feed-forward Neural Network trains the pre-processed data for voice-to-intent recognition. The proposed and developed desktop assistant does voice recognition with an accuracy of around 78.5 percent, external APIs are used to enhance user experience simplify interactions, and boost productivity. The assistant's configurable personality, which is tailored to the user's preferences, makes it stand out. It provides a high level of interactivity, enabling it to be used quickly and is very flexible regarding individual requirements. © 2024 Copyright held by the owner/author(s).

关键词： Feedforward neural networks

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A deep learning-based early alzheimer’s disease detection using magnetic resonance images

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Multimedia Tools and Applications 2025年第16期84卷 16561-16582页

作者： Suchitra, S. Krishnasamy, Lalitha Poovaraghan, R.J. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R &D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Information Technology Nandha Engineering College Tamil Nadu Erode India Department of Information Technology Jaya Engineering College Tamil Nadu Thiruninravur Chennai India

Alzheimer’s Disease (AD) is a degenerative, chronic condition of the brain for which there is now no effective treatment. However, there are medications that can slow its development. In order to stop and control the development of AD, earlier diagnosis of the disease is quintessential. Our proposed method’s primary objective is to establish a comprehensive model for the prior detection of Alzheimer’s disease and the categorization of distinct AD stages. This work employs a deep learning methodology, especially CNN. The proposed approach makes use of well-known models that have already been trained to classify medical images, like the EfficentNetB7 model, by applying the transfer learning principle. In order to achieve greater accuracy, convolutional neural networks (CNNs) are frequently scaled up as new resources become available at a fixed cost throughout the construction phase. A compound coefficient is used by the CNN architecture and scaling approach, which is the foundation of the pre-trained EfficientNetB7 model, to scale the dimensions equally. This proposed EfficientNetB7 model is quicker, easier, and more effective than other pre-trained models like VGG19 and InceptionV3. The proposed model includes simple structures that have memory requirements, provide manageable time, overfitting, and low computational complexity as well as training and inference speeds. The Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset was employed for a comprehensive assessment of the method proposed, utilizing well-known performance metrics including sensitivity, specificity, and accuracy. The findings revealed that the improvised results achieved the accuracy metric when compared to existing methods. The EfficientNetB7 model has been enhanced, and this model achieves a sensitivity of 98.08%, specificity-98%, accuracy-98.2%, and F-score-98.95%, for multi-class AD stage classifications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, par

关键词： Magnetic resonance

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Informative task classification with concatenated embeddings using deep learning on crisisMMD

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International Journal of computers and Applications 2025年第2期47卷 123-140页

作者： Jain, Tarun Gopalani, Dinesh Kumar Meena, Yogesh Department of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India

Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging platforms like Twitter(now X) have proven to be an invaluable resource for citizens to share the real-time updates in their locality. Building upon this premise, we are proposing a new hybrid robust approach that integrates transfer learning with selective features from Liwc and manually engineered features to achieve state-of-the-art results on the CrisisMMD dataset on the informative task. In our innovative approach, we harnessed the power of Deep learning combined with traditional ML practices to improve the model, we utilized OpenAI embeddings and minute details from the tweets like the sentiment, text polarity, Retweet address, links included etc. We employed a dense-dropout-based concatenate model for further processing. We established baselines of each technique individually as well as commonly used techniques like Tf-Idf, BoW, Glove embeddings. Notably, our proposed model exhibits a significant accuracy improvement, achieving an accuracy of 87.93%, surpassing the highest accuracy of traditional methods of 82.93% obtained with Tf-Idf and ensemble voting. Through this work, we conclude that there is a scope of improvement in disaster-related NLP work with the introduction of transformer-based architecture and Pretrained GPT which include a massive corpus of text improving the semantic coherency which in turn lead to higher evaluation metrics. This work in future can be extended to incorporate different embeddings as well as using techniques like Multi-headed attention as well as RNNs like GST, GRU, etc. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

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Digital Government: Research and Practice 2024年第4期5卷 1-21页

作者： Billiris, Grace Gill, Asif Oppermann, Ian Niazi, Mahmood Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Computer Science University of Technology SydneyNSW Australia University of Technology SydneyNSW Australia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents - the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this article aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of 10 legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study. © 2024 Copyright held by the owner/author(s).

关键词： Generative Artificial Intelligence Copyright Concern GAI Governance Copyright Regulations Independent Intellectual Effort Authorship

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Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

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computer Modeling in engineering & sciences 2024年第10期141卷 443-458页

作者： Prasanalakshmi Balaji Omar Alqahtani Sangita Babu Mousmi Ajay Chaurasia Shanmugapriya Prakasam Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61451Saudi Arabia College of Science and Arts in Rijal Alma King Khalid UniversityAbha61451Saudi Arabia Department of Information Technology Muffakham Jah College of Engineering and TechnologyHyderabad500034India Department of Computer Science and Business Systems Rajalakshmi Engineering CollegeThandalam602105India

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.

关键词： Bidirectional long short-term memory breast cancer detection feature extraction histopathology biopsy images multi-scale dilated vision transformer

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An Improved Intrusion Detection system for securing academic information on the cloud using a blockchain-based security framework 2

An Improved Intrusion Detection system for securing academic...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dubey, Krishna Bihari Goyal, Mukta Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

The concept of cloud computing has hastily grown in reputation, allowing businesses to shop and get access to facts from remote servers. Higher educational establishments have additionally adopted cloud services for storing and dealing with academic statistics. But this additionally poses a capability chance to the security and privacy of touchy facts. To deal with those challenges, we advise an advanced intrusion detection system (IDS) to secure the cloud's academic data. This ID uses a blockchain-based total safety framework to ensure the safety and integrity of stored statistics. The proposed IDS utilizes community visitors' monitoring and analysis to stumble upon anomalies and potential protection breaches. It also employs machine-learning techniques like anomaly detection algorithms to identify suspicious activities deviating from everyday behaviour styles. In the event of a detected intrusion, the device triggers an alert to the responsible parties and makes essential movements to mitigate the assault. The combination of blockchain technology offers an additional layer of security by developing an immutable and decentralized ledger to report all data transactions. This guarantees the integrity and authenticity of the academic statistics stored on the cloud. In case of any modifications or changes to the records, the blockchain will record them, creating a transparent audit path for accountability. Furthermore, the proposed IDS also consists of a belief management gadget that evaluates cloud carrier providers' popularity and protection posture. © 2024 IEEE.

关键词： Authenticity Cloud Community Decentralized Detected

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How graph convolutions amplify popularity bias for recommendation?

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Frontiers of computer science 2024年第5期18卷 121-132页

作者： Jiajia CHEN Jiancan WU Jiawei CHEN Xin XIN Yong LI Xiangnan HE School of Information Science and Technology University of Science and Technology of ChinaHefei 230026China School of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computer Science and Technology Shandong UniversityQingdao 250100China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China

Graphconvolutional networks(GCNs)have become prevalent in recommender system(RS)due to their superiority in modeling collaborative *** improving the overall accuracy,GCNs unfortunately amplify popularity bias-tail items are less likely to be *** effect prevents the GCN-based RS from making precise and fair recommendations,decreasing the effectiveness of recommender systems in the long *** this paper,we investigate how graph convolutions amplify the popularity bias in *** theoretical analyses,we identify two fundamental factors:(1)with graph convolution(i.e.,neighborhood aggregation),popular items exert larger influence than tail items on neighbor users,making the users move towards popular items in the representation space;(2)after multiple times of graph convolution,popular items would affect more high-order neighbors and become more *** two points make popular items get closer to almost users and thus being recommended more *** rectify this,we propose to estimate the amplified effect of popular nodes on each node's representation,and intervene the effect after each graph ***,we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node,then remove the effect from the node embeddings at each graph convolution *** method is simple and generic-it can be used in the inference stage to correct existing models rather than training a new model from scratch,and can be applied to various GCN *** demonstrate our method on two representative GCN backbones LightGCN and UltraGCN,verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular *** are open-sourced^(1)).

关键词： recommendation graph convolution networks popularity bias

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Virtual Fitness Trainer using Artificial Intelligence 2024

Virtual Fitness Trainer using Artificial Intelligence

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16th International Conference on Contemporary Computing, IC3 2024

作者： Gurbuxani, Shrey Gupta, Lakshay Madan, Kapil Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

ISBN: (纸本)9798400709722

An AI wellness coach is a software program that guides users through physical health routines by leveraging MediaPipe, OpenCV, and Python features. The program uses OpenCV computer vision techniques to monitor user progress and provide feedback on procedure and frame. To prepare the video data and provide real-time assessments, MediaPipe is used. Additionally, the app makes use of machine learning algorithms to provide users with tailored wellness recommendations and promote adoption. When these advances are combined, clients have an incredibly intelligent and practical way to advance their physical fitness. © 2024 Copyright held by the owner/author(s).

关键词： Python

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