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Communication & Computing (WCONF), World Conference on

作者： Ridam Aditya Sinha Namita Munjal Anushka Lakra Ramneek Kaur Bhakti Mehndiratta Aman Kumar Dept. of Computer Science (Specialization in Block Chain Technology) Chandigarh University Mohali India Deptt. of Mechanical Engineering Chandigarh University Mohali India

Automation anywhere, is the process of using technology to perform certain tasks or processes that could otherwise be done or performed by humans. Automation is leading to transformation of various processes and industries, and the combination of AI and VR in automation has opened new possibilities and paths for businesses and organizations to optimize their operations. Machine learning (ML) algorithms can process and work on vast amounts of data and generate valuable insights that can help us perform certain tasks and inform decision-making algorithms about it. VR which is another evolving technology can enhance the experience of the user and facilitate remote collaboration. This paper depicts about Artificial Intelligence (AI), Machine Learning (ML), Virtual Reality (VR) and Automation in industries, and it also tries to demonstrate the audience about how these mechanisms are related to each other and how they can be more effective when they work together and can give competitive and industrial advantage. The aim of this review paper is to explore the latest research and developments in automation, virtual reality, and artificial intelligence, with a particular focus on their applications in various industries and their implications for society as a whole.

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The Influence of Kanban Agile Methodology on Software Project Management: A Survey Method

The Influence of Kanban Agile Methodology on Software Projec...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Irfan Ahmed Fatima Munir Fouzia Nafees Jabar Mahmood Shehzad Ashraf Chaudhry Maryam Javaid Khadija Batool Dept. of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi U.A.E Dept. of Computer Sciences International Institute of Science Arts and Technology Gujranwala Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Software project management (SPM) is an important process in the software development (SD) industry, that involves the planning, organizing, and overseeing software application development. Kanban agile (KA) methodology is a systematic project management approach that prioritizes workflow optimization, clear visibility, and continuous delivery in SD. It has become increasingly popular in software development for its ability to adapt to changing requirements and promote continuous delivery. It is examined that kanban affects critical project management factors. Data is gathered through a survey of software professionals who are experienced with kanban. The results indicate that kanban significantly improves team collaboration, workflow transparency, and overall project efficiency. This study investigates the impact of the KA methodology on SPM using a survey-based approach. However, the study also identifies challenges, including the initial learning curve and resistance to change within teams. These findings provide valuable insights for organizations considering kanban and contribute to the broader understanding of its role in SPM. We surveyed several software development companies, and the results show that almost all of them use agile methodology (Kanban), which improves SPM as compared to other agile methodologies like Scrum, extreme programming and dynamic systems development methods.

关键词： Surveys Industries Resistance Systematics Project management Software Planning Scrum (Software development) Programming profession Software development management

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Temara, Sheetal Samanthapudi, Sagar Varma Rohella, Piyush Gupta, Ketan Ashokkumar, S.R. University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Centre For Blockchain and Cyber Security Department of Computer and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure. © 2024 IEEE.

关键词： Artificial intelligence

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Banothu Seva Ratnesh Jain Neha Khare Hussain Ahmed Choudhury M Ayyappa Chakravarthi Nitin Tomer Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Design GGITS Jabalpur M.P India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P India Department of CSE Barak Valley Engineering College Karimganj Assam India Dept. of CSE – Data Science KKR & KSR Institute of Technology and Sciences Guntur AP India Dept of IOT Cyber Security & Blockchain GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets.

关键词： Deep learning Adaptation models Limiting Computational modeling Weapons Evolutionary computation Network security Personnel Protection Monitoring

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Combining Principal Component Analysis and Template Matching for Effective EEG Signal Classification in Seizure Detection

Combining Principal Component Analysis and Template Matching...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Geetanjli Khambra Yasha Dubey Sudarshan Goswami Prashant Kumar Shukla Bylapudi Rama Devi Shailesh Shivaji Deore Department of Computer Science and Applications The Bhopal School of Social Sciences (BSSS) College Bhopal M.P. India Dept of IOT Cyber Security & Blockchain GGITS Jabalpur M.P. India Department of Computer Science and Engineering IES College of Technology Bhopal Madhya Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Dept. of ECE Anil Neerukonda Institute of Technology & Sciences Visakhapatnam AP India Department of Computer Engineering SSVPS B S Deore College of Engineering Dhule Maharashtra

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

EEG is routinely used in the assessment of the neurological patient’s state, especially in identifying the episodes of seizure in cases of epilepsy. However, the classification of the training signals into item classes is not always accurate because of the fact that they are based on EEG signals and the data is high dimensionality and complexity. This research work develops an original algorithm incorporating the PCA and template matching models for accurate EEG indicator organization in seizure recognition. PCA is used to transform the EEG signals into a space where relevant features are retained, and noise is minimized. Then, the signals are classified by using template matching technique with seizure and non-seizure templates. The integration of these two methods improves the detection performance and reduces the computational time when analysing a large amount of seizure data as shown from the experimental outcomes using benchmark EEG data. The approach proposed here has potential for real time seizure monitoring and can provide potential avenue towards enhanced clinical diagnosis and patient care.

关键词： Accuracy Sensitivity Epilepsy Pattern classification Transforms Electroencephalography Real-time systems Discrete wavelet transforms Reliability Principal component analysis

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta Ashokkumar S. R Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments & Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer and Communication Engineering Centre for Blockchain and Cyber security Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

关键词： Sentiment analysis IEEE merchandise Customer services Decision making Power control Customer satisfaction Process control

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Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

arXiv

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arXiv 2023年

作者： Zhai, Shengfang Dong, Yinpeng Shen, Qingni Pu, Shi Fang, Yuejian Su, Hang School of Software and Microelectronics Peking University China Department of Computer Science & Technology Tsinghua University China ShengShu Beijing China School of Software and Microelectronics National Engineering Research Center for Software Engineering PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China Dept. of Comp. Sci. and Tech. Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing100084 China

With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels. Specifically, we perform backdoor attacks on three levels of the vision semantics: Pixel-Backdoor, Object-Backdoor and Style-Backdoor. By utilizing a regularization loss, our methods efficiently inject backdoors into a large-scale text-to-image diffusion model while preserving its utility with benign inputs. We conduct empirical experiments on Stable Diffusion, the widely-used text-to-image diffusion model, demonstrating that the large-scale diffusion model can be easily backdoored within a few fine-tuning steps. We conduct additional experiments to explore the impact of different types of textual triggers, as well as the backdoor persistence during further training, providing insights for the development of backdoor defense methods. Besides, our investigation may contribute to the copyright protection of text-to-image models in the future. Our Code: https://***/sf-zhai/BadT2I. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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