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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Gunda, Pavan Komati, Thirupathi Rao Gitam School of Technology GITAM Deemed to be University Department of Computer Science & Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798331540364

Electronic devicerecommendations on e-commerce sites enhance the user experience by assisting users in finding products that suit their requirements, interests, and preferences. By providing them with pertinent options, it helps users who are looking for specific gadgets save time and effort. Electronic devices commonly saw shorter product lifecycles due to the regular introduction of updated and new models. Users may feel under pressure to update to newer versions in order to access the newest features or upgrades, which could cause obsolescence problems. Previously NLP techniques are utilized for recommending electronic gadgets. Biased recommendations may result from NLP models inheriting biases found in the training set of data. As a result, there may be unfair or discriminatory consequences, such as more product recommendations for particular demographic groups or the reinforcement of preexistingpreconceptions. GPT may be used to construct chatbots or AIs that engage with customers using natural language. These assistance systems driven by AI are able to help customers make purchases, answer their questions, and make personalized product suggestions. Companies can improve the customer experience by responding quickly and giving useful information. One can use GPT to simplify customer service tasks like answering common questions and fixing problems right away. This can help companies help customers around the clock, speed up reaction times, and give human support workers less work to do. Support systems driven by GPT can also learn from exchanges with customers over time, making them better at what they do. High-quality material may be produced by GPT for use in blog entries, social media postings, marketingand copy, including product descriptions. One can use this material to get people interested in and buying from your e-commerce sites, show off product attributes and advantages, and attract new customers. We can also make GPT-generated content search engine

关键词： Sales

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Women Infertility: A Systematic Review of Effects and Causes

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SN computer science 2023年第2期4卷 103页

作者： Sahana Devi, K.J. Yendapalli, Vamsidhar Department of Computer Science and Engineering GITAM School of Technology Bengaluru GITAM University Karnataka Bengaluru India

Recently, infertility has been affecting a large number of women than men. It is the male or female reproductive system’s disease. Consequently, after 12 months or more of usual insecure sexual intercourse, if they fail to attain pregnancy then that failure is defined as infertility. As per the National Institute of Child Health and Human Development, people in the USA have infertility issues that are found in 11% of women along with 9% of men. Women are more fertile in their 20s, while in their 30s it reduces to half of it. After 35 years, the probability of getting pregnant is diminished in women. For the woman to get pregnant, the proper functioning of the ovaries, fallopian tubes, and uterus is required. So, infertility occurs due to any issues with the above body parts. Women's infertility is caused by many factors, but the most vital cause is polycystic ovary syndrome (PCOS), which is a hormonal disorder commonly found among reproductive-aged women. So, women infertility, effects of women infertility, causes of women infertility, PCOS and detection of PCOS by general and machine learning (ML) techniques had been discussed in this paper. The accuracy attained in the detection of PCOS utilizing ML techniques is analyzed. Among people as of the top five responding countries that incorporate the USA, UK, Australia, India, together with the Philippines, the percentage of women with physician-assured PCOS against women exclusive of PCOS is analyzed. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Causes Detection of PCOS Effects ML techniques PCOS Women infertility

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Using DTL-MD with GANs and ResNet for Malicious Code Detection

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 63-78页

作者： Li, Yiming Xie, Tao Mei, Dongdong Department of Computer Science and Technology School of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature extraction process, the weighted sum method of GIST and LBP features is used to combine the advantages of the two features. Online transfer learning is used to reduce the data distribution difference between the target domain and the source domain. The model uses ResNet50V2 as the backbone network and combines SimAM to enhance the feature extraction and representation capabilities. In addition, in order to further improve the robustness of detection, GAN is used to generate malicious code variants and expand the training data set. In the experiment, the public CICIDS 2017 data set is used for model training and testing. The performance test results show that when the threshold is 0.7, the accuracy of DTL-MD is 95.8% and the F1 score is 0.93. In a performance test involving 30,000 samples, the throughput of the DTL-MD model under Trojans, viruses, worms, and adware is 11, 12, 11, and 12 tasks/s, respectively, and the inference time is 211, 225, 239, and 234 samples/s, respectively. Compared with GAN, DTL-MD increases the throughput by about 10% and the inference speed by about 15%. The research aims to provide new ideas for improving the intelligence and automation level of malicious code detection technology, which has certain application value and practical significance. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Feature Selection

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MLRT-UNet:An Efficient Multi-Level Relation Transformer Based U-Net for Thyroid Nodule Segmentation

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computer Modeling in engineering & sciences 2025年第4期143卷 413-448页

作者： Kaku Haribabu Prasath R Praveen Joe IR Department of Computer Science and Engineering RMK College of Engineering and TechnologyTiruvallur601206India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.

关键词： Thyroid nodules endocrine system multi-level relation transformer U-Net self-attention external attention co-operative transformer fusion thyroid nodules segmentation

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Taxonomy for Classification of Cloud Service (Paas) Interoperability 1

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World Conference on Information Systems for Business Management, ISBM 2022

作者： Adhoni, Zameer Ahmed Dayanand Lal, N. Department of Computer Science and Engineering Gitam School of Technology Gitam University Bangalore India

ISBN: (数字)9789811974472

ISBN: (纸本)9789811974465

The decreasing cost of hardware has opened the doors to the development of various concepts like big data and huge storage spaces. The development of the cloud is one major development in this string of developments. The economic and other important contract features like the locking time gave rise to the usage of multi-clouds by various companies for the same or similar applications. The extended form of this scenario leads to operation on data on different clouds giving rise to interoperability. The Infrastructure as a service, Platform as a service, and Software as a service in the cloud force us to define taxonomy based on various views. Each view addresses its own customized problem. There is a need to generalize the taxonomy of interoperability. This paper presents a taxonomy for the classification of the cloud for Platform as a Service. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interoperability

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Predicting Stock Market Prices Using a Hybrid of High-Order Neural Networks and Barnacle Mating Optimization 7th

Predicting Stock Market Prices Using a Hybrid of High-Order ...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Behera, Sudersan Pavan Kumar, A.V.S. Nayak, Sarat Chandra Department of Computer Science and Engineering School of Engineering and Technology GIET University Gunupur India Department of Computer Science and Engineering School of Technology GITAM Hyderabad Campus Hyderabad India

ISBN: (纸本)9789819720880

Predicting stock market movements presents a formidable challenge due to the inherent nonlinearity and ever-changing nature of financial markets. In this research endeavor, we employ an innovative approach, harnessing the power of an evolutionary algorithm called barnacle mating optimization (BMO), to fine-tune the optimal parameters of a sophisticated neural network known as the Pi-Sigma neural network (PSNN). This intricate optimization process results in the development of a hybrid model, aptly named BMO-PSNN. We put BMO-PSNN to the test by utilizing it for predicting the closing prices of five widely tracked stock indices. In order to provide a comprehensive comparison, we also implement the traditional gradient descent (GD) optimization technique to train the PSNN network for the same predictive task. The performance evaluation is carried out using the average percentage error (APE) metric. Remarkably, the results conclusively demonstrate that BMO-PSNN outshines GD-PSNN in its ability to make more accurate predictions, underscoring the effectiveness of the evolutionary BMO algorithm in tackling the complexities of stock market forecasting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Costs

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Levy Flight Adopted Particle Swarm Optimization-based Resource Allocation Strategy in Fog Computing

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Chinese Journal of Electronics 2023年

作者： Sharmila Patil(Karpe) Brahmananda S H Research Scholar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed University Professor Department of Computer Science and Engineering GITAM Bengaluru

The prevalence of the Internet of Things(Io T) is unsteady in the context of cloud computing, it is difficult to identify fog and cloud resource scheduling policies that will satisfy users' Qo S need. As a result, it increases the efficiency of resource usage and boosts user and resource supplier profit. This research intends to introduce a novel strategy for computing fog via emergencyoriented resource allotment, which aims and determines the effective process under different parameters. The modeling of a non-linear functionality that is subjected to an objective function and incorporates needs or factors like Service response rate, Execution efficiency, and Reboot rate allows for the resource allocation of cloud to fog computing in this work. Apart from this, the proposed system considers the resource allocation in emergency priority situations that must cope-up with the immediate resource allocation as well. Security in resource allocation is also taken into consideration with this strategy. Thus the multi-objective function considers 3 objectives such as Service response rate, Execution efficiency, and Reboot rate. All these strategies in resource allocation are fulfilled by Levy Flight adopted Particle Swarm Optimization(LF-PSO). The evaluation is performed to determine whether the developed strategy is superior to numerous traditional schemes. The cost function attained by the adopted technique is 120, which is 19.17%, 5%, and 2.5%greater than the conventional schemes like GWSO, EHO,and PSO, when the number of iterations is 50.

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Application Controlling Using Hand Gestures Through Yolov5s 8th

Application Controlling Using Hand Gestures Through Yolov5s

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8th International Conference on computers, Management and Mathematical sciences, ICCM 2022

作者： Rajendra Prasad, K. Tarun Naga Sai, Chadaram Bhaskar Ganesh, Guntuboina Rohini Raghavi, Baina Sri Saketh, Gunnam Computer Science Engineering Gitam School of Technology Gandhi Institute of Technology and Management Gitam Visakhapatnam India

ISBN: (纸本)9783031511660

With the aid of Python, OpenCV, Yolov5s, and PyAutoGui, we attempt to use hand gestures for controlling an application. The need for unfettered interaction has made it difficult for conventional input devices like the keyboard and mouse to be used. In linguistics, gestures with the hand are a crucial part of body language (Nagalapuram GD, Roopashree S, Varshashree D, Dheeraj D, Nazareth DJ, Controlling media player with hand gestures using convolutional neural network. In: 2021 IEEE Mysore sub section international conference (MysuruCon). 978-1-6654-3888-9/21/$31.00 ©2021 IEEE, https://***/10.1109/MYSURUCON52639.2021.9641567, 2021). Using the hand as a device makes it simple to interface with humans and computers. It would be intriguing to engage with machines using hand gestures (Li G, Li D, Yang A, Real-time hand gesture detection based on Yolov5s. In: Proceedings of the 41st Chinese control conference, July 25–27, Hefei, China, 2022). Different applications, Various software programs, including Media Player, PowerPoint, PDF viewers, and software that accepts keyboard and mouse input, are controlled by hand gestures. Interaction is simple, practical, and requires no tedious additional equipment when gestures are used (Paliwal M, Sharma G, Nath D, Rathore A, Mishra H, Mondal S, A dynamic hand gesture recognition system for controlling VLC media player. In: 2013 international conference on advances in technology and engineering (ICATE). IEEE, 2013). A camera-based hand detection system that employs a live webcam stream to identify the gesture once it has been observed. This system gives the user access to essential keyboard functions with the aid of the PyAutoGui package. With this technology, we can increase the usability of computers for people with mobility issues or who cannot have physical contact with peripherals. A deep learning algorithm called Yolov5s is used for object detection. The precision and recall are near to 1, the mAP (0.5) value for hand gest

关键词： Deep learning

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Automated Detection and Classification of Cotton Leaf Diseases: A computer Vision Approach

Automated Detection and Classification of Cotton Leaf Diseas...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Ganguly, Swastick Jathin, S. Mohak, M. Sanjay, M. Somashekhar, B.M. Shobharani, N. Amrita School Of Computing Department Of Computer Science Mysuru Campus India Gitam School Of Technology Gitam University Dept Of Computer Science And Engineering Bengaluru Campus India

ISBN: (纸本)9798350371567

Cotton plays a vital role in agricultural sustainability, extending beyond its textile applications. Pathogens pose significant threats to cotton plants and farmers' livelihoods, highlighting the critical need for disease detection to maintain crop health and ensure optimal yields. Our research pioneers a novel approach to disease detection in cotton through advanced image analysis techniques. This involves exploring deep learning methodologies specifically tailored for detecting cotton leaf diseases and enhancing accuracy by integrating CNN models, while balancing labor intensity and cost considerations. Leveraging the Convolutional Neural Networks (CNN), the combination of the interpretability of Densenet 121 with the stability of ResNet 50, assessing the strength of their combined algorithm. This hybrid model accurately classifies various cotton plant diseases, enabling fine-grained analysis. With a precision-driven approach, the proposed model achieves exceptional results, accurately identifying six distinct diseases, including fungal, bacterial, and viral pathogens, with an impressive 96% accuracy rate. The research aims to provide a comprehensive and dependable solution for disease management in the cotton industry, ultimately promoting crop health, productivity, and sustainability. © 2024 IEEE.

关键词： Cotton

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NeurDB: an AI-powered autonomous data system

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science China(Information sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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