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6th Smart Cities Symposium, SCS 2022

作者： Bajaj, Karan Sharma, Bhisham Singh, Raman Kumar, Mukesh Chowdhury, Subrata Department of Computer Science & Engineering Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India School of Computing Engineering and Physical Sciences University of the West of Scotland Lanarkshire United Kingdom School of Computer Application Lovely Professional University Punjab Phagwara India MCA Department Sri Venkateswara College of Engineering and Technology India

ISBN: (纸本)9781839538544

Data processing at high speeds and storage solutions on a massive scale are two things that are becoming increasingly important as a result of the proliferation of new start-up companies and the vast volume of data that users consume daily. The cloud platforms offer a solution to these issues in the shape of virtualization, which entails the creation of several distinct virtual machines within a single actual system. Because of this, the efficiency of the processor increases, and it now has far less downtime. The four major cloud platforms are discussed, and each has its own set of benefits, which helps make them successful in their unique conducts. Although Amazon EC2 is one of the most used and earliest that offers assistance for the utmost numeral of already configured system software termed as operating systems, which falls short in terms of both its scope and the support that it makes available. In a similar vein, the Google Cloud Platform can support the greatest number of databases and has an excellent repository of in-built libraries. The Microsoft Azure has the greatest number of users out of four, however, it is badly lacking in the database supports it offers. IBM Watson offers user friendly services but has scalability issues. As a result, a conclusion has been reached, that the selecting a particular cloud platform, there is extreme dependence on the needs of the users. These requirements may vary from client to client which are the end-users of the applications. Before deciding on a particular platform, it is best to conduct a thorough analysis of the needs. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Digital storage

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Navigating the Web of Disinformation and Misinformation: Large Language Models as Double-Edged Swords

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IEEE Access 2024年 1-1页

作者： Shah, Siddhant Bikram Thapa, Surendrabikram Acharya, Ashish Rauniyar, Kritesh Poudel, Sweta Jain, Sandesh Masood, Anum Naseem, Usman Department of Computer Science and Engineering Delhi Technological University New Delhi India Virginia Tech Blacksburg VA USA Herald College Kathmandu Sanogaucharan Naxal Bhagwatibahal Nepal Kathmandu Engineering College Tribhuvan University Kathmandu Nepal Department of Circulation and Medical Imaging Norwegian University of Science and Technology Trondheim Norway School of Computing Macquarie University Sydney NSW Australia

This paper explores the dual role of Large Language Models (LLMs) in the context of online misinformation and disinformation. In today’s digital landscape, where the internet and social media facilitate the rapid dissemination of information, discerning between accurate content and falsified information presents a formidable challenge. Misinformation, often arising unintentionally, and disinformation, crafted deliberately, are at the forefront of this challenge. LLMs such as OpenAI’s GPT-4, equipped with advanced language generation abilities, present a double-edged sword in this scenario. While they hold promise in combating misinformation by fact-checking and detecting LLM-generated text, their ability to generate realistic, contextually relevant text also poses risks for creating and propagating misinformation. Further, LLMs are plagued with many problems such as biases, knowledge cutoffs, and hallucinations, which may further perpetuate misinformation and disinformation. The paper outlines historical developments in misinformation detection and how it affects social media consumption, especially among youth, and introduces LLMs and their applications in various domains. It then critically analyzes the potential of LLMs to generate and counter misinformation and disinformation in sensitive topics such as healthcare, COVID-19, and political agendas. Further, it discusses mitigation strategies, ethical considerations, and regulatory measures, summarizing previous methods and proposing future research direction toward leveraging the benefits of LLMs while minimizing misuse risks. The paper concludes by acknowledging LLMs as powerful tools with significant implications in both spreading and combating misinformation in the digital age. Authors

关键词： Social networking (online)

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Transforming Hematological Data Interpretation: A Deep Learning and NLP Framework for Blood Cancer Prognostics

Transforming Hematological Data Interpretation: A Deep Learn...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Erupaka Nitya Vinay Kumar Nassa Anupam Singh Priyanka K. Maithili Vartika Agarwal Department of CSIT CVR College of Engineering Hyderabad India Department of Information Communication Technology(ICT} Tecnia Institute of Advanced Studies (Delhi) Delhi India Department of Computer Science and Engineering Graphic Era Hill University Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India University Institute of Computing Chandigarh University Gharuan Gharuan India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

In this research work, we have investigated the transformative potential of a deep learning and natural language processing framework in enriching the capabilities to prognosticate blood cancer. Specifically, we used a dataset that covers a complete range of a clinical, high-resolution blood smear, bone marrow biopsy and imaging such as CT, MRI and PET scans. To achieve our objectives, we also designed and implemented hybrid models of CNNs and LSTM networks, specifically VGG 19 + LSTM, VGG 16 + LSTM and ResNet 50 + LSTM. The results of the study showed that the VGG 19 + LSTM model achieved the highest accuracy than the VGG 16 + LSTM and ResNet 50 + LSTM models. The confusion matrix, precision, recall and F1 score metrics gave a clear indication of the fact that VGG 19 + LSTM has the highest capacity predictive capabilities. During the calculation of the confusion matrix, this model showed the minimum number of false positives and false negatives. We also noticed that all models converge during the loss and accuracy in every epoch, which suggests that the model has the potential to learn from the complex data. Through the extensive utilisation of clinical data with imaging, we designed reliable diagnostic tools that have more chances to prognosticate accurately. Moreover, it has made a significant contribution to the validation of deep learning in interpreting medical data. Most importantly, it will also give a kind of approach to future work to further extend these methods for other cancers.

关键词： Deep learning Measurement Accuracy Data models Natural language processing Positron emission tomography Long short term memory Blood Medical diagnostic imaging Cancer

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Convolutional Neural Network Model for Traffic Sign Recognition

Convolutional Neural Network Model for Traffic Sign Recognit...

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Advance computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： B Narendra Kumar Rao R Ranjana Nagendra Panini Challa S. Sreenivasa Chakravarthi School of Computing Mohan Babu University Tirupati India Dept of IT Sri Sairam Engineering College Chennai Tamil Nadu India School of Computer Science and Engineering (SCOPE) VIT-AP University Amaravati India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidayapeetham Chennai India

Despite the fact that traffic sign segmentation has been publicized for a long time, the majority of past research has centered on traffic signs using graphics. This study aims to propose method for evaluating all sorts of traffic signals based on observations, including in surveillance video streams captured by a vehicle camera, both icon and word signs. Extraction of traffic sign areas of interest (ROIs), exploration and sentiment analysis of ROIs, and post-processing are the three steps of the system. To identify the Regions of interest from each shot of road markings, the most stable fractal dimension regions on grey and calibrated RGB channels are employed. The researchers suggest a multi-convolutional neural network that was trained using a large quantity of data, including fake road markings and pictures extracted from real-time scenarios, Then it refines and emphasizes them based on the most recent their descriptions. Finally, post-processing gathers all of the investors when making an isolating pronouncement. The utility of the suggested technology has been shown through experimental data. This effort aims to improve highest level of quality by flashing drivers about hazards and risks.

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Analysis of Cyber Security Threats in Payment Gateway Technology 3

Analysis of Cyber Security Threats in Payment Gateway Techno...

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3rd International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Sharma, Hari Prapan Krishna, Somanchi Hari Prakash, R Vijay Tiwari, Mohit Tiwari, Tripti Kumar, Madarapu Naresh GLA University Institute of Business Management Uttar Pradesh Mathura India Vignana Bharathi Institute of Technology Department of Business Management Aushapur Village Ghatkrsae Mandal Malkajgiri Medchal Dist501301 India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A 4 Rothak Road Paschim Vihar Delhi India BVIMR Department of Management Studies A 4 Rothak Road Paschim Vihar Delhi India National Informatics Centre Ministry of Electronics and Information Technology Delhi India

ISBN: (纸本)9798350399264

There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenience for online shoppers. Cyber-attack techniques, on the other hand, are developing at the same rapid pace as security mechanisms. The authors of this paper examine the history of electronic payment systems, from the development of the corresponding language through the emergence of today's standard electronic payment systems. It also reveals a lack of security measures and approaches to fixing the problem. The current survey research makes a significant contribution by outlining the state of the electronic payment system framework and the possibilities it presents for the development of e-commerce in the future. The rates of suspicious purchases, which will serve as a yardstick in the creation of a trustworthy e-payment system, have been briefly discussed and analyzed. © 2023 IEEE.

关键词： Cybersecurity

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Safe Probabilistic Invariance Verification for Stochastic Discrete-Time Dynamical Systems

Safe Probabilistic Invariance Verification for Stochastic Di...

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IEEE Conference on Decision and Control

作者： Yiqing Yu Taoran Wu Bican Xia Ji Wang Bai Xue Department of Information Science School of Mathematical Sciences Peking University China State Key Laboratory of Computer Science Institute of Software CAS China State Key Laboratory of High Performance Computing and College of Computer Science and Technology National University of Defense Technology China

Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-time dynamical systems subject to stochastic disturbances over the infinite time horizon. Our goal is to compute the lower and upper bounds of the liveness probability for a given safe set and set of initial states. This probability represents the likelihood that the system will remain within the safe set for all time. To address this problem, we draw inspiration from stochastic barrier certificates for safety verification and build upon the findings in [21], where an equation was presented for exact probability analysis. We present two sets of optimizations and demonstrate their effectiveness through two examples, using semi-definite programming tools.

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Content-Based Technical Solution for Cyberstalking Detection 3

Content-Based Technical Solution for Cyberstalking Detection

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3rd International Conference on computer Communication and the Internet, ICCCI 2021

作者： Asante, Audrey Feng, Xiaohua Catholic University College of Ghana Department of Computing and Information Sciences Fiapre-Sunyani Ghana School of Computer Science and Technology University of Bedfordshire Luton United Kingdom

ISBN: (纸本)9781728176185

The continued usage of technology has led to the rise of cyberstalking. Cyberstalking is seen as traditional method of stalking that has been altered by technology. This crime has now been modernized using technological tools and techniques. The continued increase in cyberstalking in the world today has drawn attention to the need to address this problem. Though studies on this crime have been conducted in the fields of criminology, legal, public health, sociology, and psychology, it still remains a challenge to detect, prevent, and investigate this crime. Traditional stalking methods have been used to combat it, despite the fact that this crime is committed online. Unfortunately, these methods have provided few solutions for detecting and preventing it. The prevalence of this crime, combined with technological advancement, has necessitated the development of technical strategies to mitigate it, protect victims, and assist law enforcement agencies. In this study, a content-based detection framework for cyberstalking is proposed. The framework consists of message identification, filtering, detection (content detection and profiling offender) and evidence modules. It is designed as a forensic readiness framework that can automatically detect cyberstalking, gather evidence and profile potential offenders. The framework employs machine learning, data mining techniques, digital forensics, and profiling to analyze text, image, and media contents, collect evidence, and profile offenders. This framework would not only detect cyberstalking automatically, but it would also be useful as an investigative tool for law enforcement. © 2021 IEEE.

关键词： Machine learning

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Bibliometric Analysis of Trend in Metaverse Research

Bibliometric Analysis of Trend in Metaverse Research

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International Conference on Information Management and Technology (ICIMTech)

作者： Cadelina Cassandra Mohamad Noorman Masrek Fadhilah Aman Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia School of Information Science College of Computing Informatics and Media University Teknologi MARA Shah Alam Selangor MALAYSIA

Metaverse is currently going to be a popular topic. This study aims to investigate the research trends in metaverse topics to provide a new point of view for future research direction. This study performed a bibliometric analysis systematic literature review by reviewing the specific term in the Scopus database. The data were obtained from publish or perish app and analyzed using the VOSviewer Software. This research article was limited from 2018 - 2022, and 119 were analyzed using the VOSViewer Software. The result shows that the metaverse topic is increasing very fast in 2022. The most popular term in the title and abstract combination are Experience, Study, Application, and Education, which indicates the opportunity for future research, trend, and topic that the public and researchers are interested in. Meanwhile, the keyword of a metaverse in education is still limited, which opens new opportunities to be discussed in the future.

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Distinguishability Calibration to In-Context Learning

arXiv

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arXiv 2023年

作者： Li, Hongjing Yan, Hanqi Li, Yanran Qian, Li He, Yulan Gui, Lin Department of Computer Science University of Warwick United Kingdom Xiaomi AI Lab China Department of informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. When using prompt-based learning for text classification, the goal is to use a pretrained language model (PLM) to predict a missing token in a pre-defined template given an input text, which can be mapped to a class label. However, PLMs built on the transformer architecture tend to generate similar output embeddings, making it difficult to discriminate between different class labels. The problem is further exacerbated when dealing with classification tasks involving many fine-grained class labels. In this work, we alleviate this information diffusion issue, i.e., different tokens share a large proportion of similar information after going through stacked multiple self-attention layers in a transformer, by proposing a calibration method built on feature transformations through rotation and scaling to map a PLM-encoded embedding into a new metric space to guarantee the distinguishability of the resulting embeddings. Furthermore, we take the advantage of hyperbolic embeddings to capture the hierarchical relations among fine-grained class-associated token embedding by a coarse-to-fine metric learning strategy to enhance the distinguishability of the learned output embeddings. Extensive experiments on the three datasets under various settings demonstrate the effectiveness of our approach. 1 © 2023, CC BY.

关键词： Embeddings

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