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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Rahim, Rahila Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350372229

The world of Industry 4.0 embraces the transformation that calls for strategic investments, cooperative partnerships, and a proactive approach to overcome obstacles to successfully transition to smarter, more connected, and more efficient in- dustrial ecosystems. The accounting and finance industry has benefited greatly from process modernization, which has resulted in a significant decrease in the use of the traditional system through the integration of digital technologies. The use of various artificial intelligence technologies such as expert systems for audit and taxation, intelligent agents for customer service, Natural Language Processing (NLP), Robotic Process Automation (RPA), Blockchain, data analytics, and machine learning for decision- making has been proposed to automate, streamline, and improve the efficiency of various tasks and processes in accounting and finance operations. The study specifically investigates how the incorporation of AI technologies affects the reliability & timeliness of financial & accounting processes. The paper also presents some use cases and AI applications relevant to this field. © 2024 IEEE.

关键词： accounting AI finance ML NLP

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Exploring Dual-Module Approaches for Sentiment Analysis in Call Recordings 5

Exploring Dual-Module Approaches for Sentiment Analysis in C...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Dhobale, Nikita Dinkar Elle, Krisha Ramesh Deshmukh, Pratiksha Vilas Nirne, Sakshi Sidram Jarali, Prof. Ashwini International Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350379945

The system entails the development of a sentiment analysis system consisting of two integral modules aimed at analysing audio recordings of calls. The first module predicts sentiment-positive, negative, or neutral-by converting speech to text using speech-to-text APIs and subsequently applying machine learning algorithms including Random Forest, Naive Bayes, SVM, and Logistic Regression. The second module employs audio processing techniques utilising Librosa for feature extraction and Convolutional Neural Networks (CNNs) for emotion recognition. The system facilitates comparisons between the results obtained from both modules for call recordings. Users, starting with system administrators, can choose between two options: browsing for uploading pre-recorded audio files or live recording. In both scenarios, the system executes sentiment analysis and emotion detection, presenting results derived from speech-to-text and audio processing analyses. © 2024 IEEE.

关键词： Emotion Recognition

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Enhancing Hydroponic Farming Productivity Through IoT-Based Multi-Sensor Monitoring System 9

Enhancing Hydroponic Farming Productivity Through IoT-Based ...

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9th International Conference on Internet of Things, Big Data and Security, IoTBDS 2024

作者： Khadijah Febriana, R. Thakur, Rahul Roy, Sudip Department of Computer Science & Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9789897586996

Hydroponic farming has gained prominence in modern agriculture owing to its inherent advantages in resource efficiency and crop yield. This research explores the integration of Internet of Things (IoT) technologies to further optimize hydroponic systems by monitoring and controlling crucial solution and environmental parameters. A novel IoT-based hydroponic monitoring system has been developed, featuring a comprehensive array of sensors including solution’s temperature, acidity (pH), total dissolved solids (TDS) and electrical conductivity (EC), ambient temperature and humidity, and light intensity. This system leverages both WiFi and LoRaWAN technologies to enhance connectivity, ensuring reliable communication over extended ranges. This integration of communication protocols facilitates seamless data transmission and real-time control of hydroponic conditions. The proposed IoT-based system aims to provide growers with a comprehensive and user-friendly platform to monitor and adjust key parameters critical for plant growth, thereby maximizing the productivity and yield in hydroponic farming. The results of this study contribute valuable insights into the potential of IoT technologies to revolutionize precision agriculture and sustainable food production. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Hydroponics

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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X-Ray Based Lung Disease Classification Using Fine-Tuned VGG16 Model 2

X-Ray Based Lung Disease Classification Using Fine-Tuned VGG...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gehani, Ronit Victor, Akila Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350327533

The COVID-19 pandemic was a global outbreak of a viral that caused widespread to people's well-being. COVID-19 primarily affects the respiratory system, leading to various lung diseases and complications. During this unprecedented time, the need for accurate and efficient classification systems for lung diseases, including COVID-19, has become more crucial than ever. During the pandemic, accurate and timely identification of lung diseases became crucial in order to effectively manage the healthcare system and provide appropriate treatment to patients. Therefore, early detection using state-of-The-Art technology such as deep learning and computer vision algorithms can greatly assist in the classification of lung diseases. Concurrently there has been an unprecedented increase in the incidence of other respiratory ailments, such as Pneumonia, which further emphasizes the need for accurate and efficient classification systems. In light of this urgent requirement, this study has proposed a novel model for image classification by demonstrating the ability to differentiate COVID-19, viral pneumonia, and normal categories. The classification model presented in this study is constructed by fine-Tuning a VGG16 architecture that has undergone extensive training, validation, and evaluation processes. The proposed dataset contains a large set of images, which includes 3616 COVID-19 images, 1345 Pneumonia and 10k normal training images. According to the experimental findings, the proposed model has achieved an accuracy of 95.79%. To verify the performance, the training and validation loss curves are outlined. © 2024 IEEE.

关键词： Bayesian Optimizer COVID-19 Deep learning Feature Extraction Feature Selection Machine learning Prediction X-ray images

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Reducing Gender and Ethnicity Bias in Personality Trait Prediction 6

Reducing Gender and Ethnicity Bias in Personality Trait Pred...

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Singh, Sarika Shah, Yash Vajani, Yug Godbole, Anand Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350306613

Automatic personality trait prediction is the prediction of a person's personality from cues like facial expressions, body language, manner of speaking, etc. There has been a lot of research on implementing machine learning/deep learning models that can infer a person's personality traits, however studies have shown such models to be biased towards certain ethnicities and genders. Our work proposes the use of Adversarial Debiasing to reduce bias in such models. We apply this technique to train a CNN and an LSTM to predict personality traits from images and audio features extracted from the ChaLearn LAP APA 2016 dataset [1]. This method achieves an accuracy between 0.894-0.913 for different traits and reduces bias, as measured by Disparate Impact Ratio. © 2024 IEEE.

关键词： adversarial learning convolutional neural network debiasing fairness LSTM OCEAN personality trait VGG-16

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Spam SMS Detection Using Natural Language Processing 8

Spam SMS Detection Using Natural Language Processing

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Rajput, Satpalsing D. Chopade, Pratiksha Chivate, Atharva Chitpur, Shreeshail Dashetwar, Isha Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350391770

A spam SMS is an unsolicited text message dispatched to a large population, illegitimately, without the authorization of the recipients. Reports suggest that the number of Spam SMS received by citizens of any country is approximately 400 million, and this number is increasing day by day. These SMS messages may contain malicious links, causing security threats including phishing attacks and malware infections. Some spam SMS messages can involve fraudulent schemes, leading to financial losses for the receiver and can also cause privacy threats. Hence, there is an urge to detect these spam SMSs correctly and avoid the severe consequences for the recipients. The proposed study emphasizes the development of Natural Language Processing based Machine Learning models to identify spam SMS texts. The process involves tokenization of the SMS texts, stemming of the words, and many more abstractions of the keywords for easier classification. Firstly, the text messages are processed using NLP and then the processed data is trained on different ML algorithms. Multiple ML models are created and tested on various kinds of SMS messages for the detection of spam messages. This expert system ensures accurate and precise detection of received messages. This expert system intends to reduce the threats and safeguard the security and privacy of the consumers, by essentially recognizing and steering clear of spam SMS messages. This system aims to empower users with the tools and knowledge needed to navigate the digital landscape safely and securely. © 2024 IEEE.

关键词： Expert systems

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Person Re-Identification: A Retrospective Study, Recent Trends and Future Scope 1

Person Re-Identification: A Retrospective Study, Recent Tren...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Soni, Shashank Kumar Kumar, Gautam National Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Person re-identification (Re-ID) searches several non-overlapping cameras for a person of interest. The development of deep neural networks combined with the increasing need for intelligent video surveillance has led to a huge surge in interest in the field of computer vision. This article highlights the state-of-the-art techniques used for person re-identification (reID), including the latest advancements and challenges. It outlines the different person re-identification datasets that are available in the literature, evaluates the effectiveness of the best techniques across a range of datasets, and clarifies the important restrictions on person re-identification, including the effects of changing environmental conditions and data limitations. Furthermore, the article points out the future research direction, focusing on creating more realistic datasets and designing robust algorithms that can effectively handle real-world conditions in order to drive the growth and development of re- ID technology. © 2024 IEEE.

关键词： Deep neural networks

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Parallel Katz Centrality On Evolving Graphs 31

Parallel Katz Centrality On Evolving Graphs

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Dishit Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

Katz centrality measures a node’s influence within a network, considering the total number of walks between pairs of nodes rather than solely the shortest paths. This paper presents an algorithm for dynamically updating Katz centrality scores as the underlying graph evolves. Our method employs a frontier-based breadth-first search (BFS) to efficiently derive updated Katz scores in dynamic graphs. Notably, our algorithm accommodates node insertions and deletions, addressing a significant limitation of current state-of-the-art methods. © 2024 IEEE

关键词： Graph algorithms

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AdamW-RSO DBN: AdamW-Rat Swarm Optimized deep belief network for software vulnerability detection

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Multimedia Tools and Applications 2024年 1-23页

作者： Shaji, Lijin R, Suji Pramila Department of Computer Applications Noorul Islam Centre for Higher Education Tamil Nadu Thuckalay629180 India Department of Computer Science and Engineering Mar Baselios Institute of Technology and Science Kerala Kothamangalam686693 India

During the software development phase, vulnerability detection needs huge interest to make it less vulnerable and secure. Every time, the vulnerable software provokes hackers to make malicious activities and interrupts the software operation which results in millions in financial losses to the software firm. To minimize the losses, there are reliable and effectual vulnerability detection systems developed by security communities aspiring to recognize software vulnerabilities earlier in the development or testing stage. One such system is deep learning which can detect security vulnerabilities in the software. This work develops an optimized deep-learning system for detecting software vulnerabilities. Feature fusion approach is developed using canonical correlation analysis and Deep Residual Network (DRN) so that all the features in the input open-source projects are utilized without negligence. Moreover, an algorithm, AdamW- Rat Swarm Optimizer (AdamW-RSO), is proposed by combining AdamW and RSO, to train the DBN that is adopted to perform vulnerability detection. Finally, the detection performance is examined by utilizing metrics, such as recall, precision, and F-measure on a function-level vulnerable dataset. The test results reveal that the AdamW-RSO-based DBN has obtained superior detection performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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