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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Prashant Kumar Mishra Jagrati Singh Department of Computer Science and Engineering Centre For Advanced Studies AKTU Lucknow India

Accurate and timely classification of fresh and decaying fruits is crucial for upholding quality standards in the food industry. This study introduces a deep learning approach that utilizes images to distinguish between fresh and rotten fruits. The proposed solution effectively addresses this classification challenge by control the capabilities of Convolutional Neural Networks. In this research a diverse dataset is used, comprising labeled images of various fruits in varying states of freshness, encompassing both rotten and fresh examples. Key preprocessing steps, including resizing and normalizing the dataset, is implemented to ensure that the model receives a uniform input. The core of the methodology involves initializing a pre-trained CNN architecture. This approach enables the adaptation of the pretrained model to the specific fruit classification task, ultimately providing an efficient and effective solution for quality control in the food sector. Through the utilization of this approach, our model can inherit valuable feature representations from its pretrained counterpart. To evaluate the performance of the trained model, a distinct test dataset is employed and the results encompass key measurements of performance. With an impressive accuracy of 97.5%, the proposed model outperforms the base paper, which achieves 96.05%. This advancement highlights the model’s proficiency in distinguishing between fresh and rotten fruits, underscoring its practical effectiveness.

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The Social Media Implications on the Sales of Business Products 3

The Social Media Implications on the Sales of Business Produ...

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Jacia, Elvina Dyan, Gabriel Ifan, Amelia Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutera Department of Information System Indonesia Binus Graduate Program-Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781665464871

The use of technology nowadays does not feel strange. Everyday people who use technology for their daily needs, starting with each other, seeking knowledge, can even earn income by doing business using technology. Social media is a tool that can be used by people to become with each other by creating, sharing, and exchanging information virtually through a network. However, in the current era, the notion of social media is not only about exchanging information, but also to support business products. The problem is how to compete tightly in this modern era in increasing the selling value of business products. The positive impact of social media for sales in business products are huge such as, increase sales, increase product promotion, greater market distribution. On the other side, the negative impact also takes part such as, the more competitors, the products compete fiercely in the market. This research aims to identify more the impact of social media on sales of business products that focused on business, benefit, and factor. The research method that we used is quantitative which focuses on the number of samples of respondents to support the research results and the results are in the form of a graphic that shows the answers of the respondents to the questionnaire. © 2023 IEEE.

关键词： Sales

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Enhanced Diagnosis of Schizophrenia Using Integrated Node Embedding and Causal Discovery on Imbalanced fMRI Data 10

Enhanced Diagnosis of Schizophrenia Using Integrated Node Em...

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10th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2024

作者： Sabzevar, Sina Masoudnia, Saeed Araabi, Babak N Nazemzadeh, MohammadReza Tavakoli, Hosna University of Tehran Department of Electrical and Computer Engineering Tehran Iran Monash University Department of Neuroscience Melbourne Australia Institute of Cognitive Science Studies Computational and Artificial Intelligence Department Tehran Iran

ISBN: (纸本)9798331532543

This study introduces a novel diagnostic method for schizophrenia using causal discovery and node embedding techniques on resting-state fMRI data. Data from 148 subjects (27 schizophrenia patients, 121 healthy controls) were analyzed across 116 brain regions, applying several causal discovery techniques to assess directional connectivity and uncover schizophrenia-related patterns. By combining advanced causal discovery with node embedding techniques and a rigorous data-balancing pipeline, our method improves sensitivity to subtle network variations critical for schizophrenia diagnosis. Our approach addresses key challenges in the literature: the difficulty of extracting effective neurobiological markers from noisy and complex fMRI data, the limited interpretability of connectivity networks, and the imbalance in datasets that often skews classification performance. Experimental results demonstrate the pipeline's effectiveness in overcoming imbalanced data challenges and extracting features from noisy and complex data, with Regression Dynamic Causal Modeling (RDCM) achieving the highest classification metrics, including 90.06% accuracy and 89.09% sensitivity. This method identified 15 brain regions with strong diagnostic potential, offering improved sensitivity and specificity for schizophrenia diagnosis Compared to contemporary domain techniques. © 2024 IEEE.

关键词： Diseases

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Geometric additivity of the modular commutator for multipartite entanglement

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Physical Review B 2025年第7期111卷 075167-075167页

作者： Sung-Min Park Isaac H. Kim Eun-Gook Moon Department of Physics Korea Advanced Institute of Science and Technology Daejeon 34141 Korea Department of Computer Science University of California Davis California 95616 USA

A recent surge of research on many-body quantum entanglement has uncovered intriguing properties of quantum many-body systems. A prime example is the modular commutator, which can extract a topological invariant from a single wave function. Here, we unveil geometric properties of many-body entanglement via a modular commutator of two-dimensional gapped quantum many-body systems. We obtain the geometric additivity of a modular commutator, which indicates that the modular commutator for a multipartite system may be an integer multiple of the one for tripartite systems. Using our additivity formula, we also derive a curious identity for the modular commutators involving disconnected intervals in a certain class of conformal field theories. We further illustrate this geometric additivity for both bulk and edge subsystems using numerical calculations of the Haldane and π-flux models.

关键词： Quantum entanglement

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Towards Insider Threat Resilience: A Proposed Mitigation Model 4

Towards Insider Threat Resilience: A Proposed Mitigation Mod...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Kothari, Navyansh Bhardwaj, Chanakya Mishra, Shruti Satapathy, Sandeep Kumar Cho, Sung-Bae Mallick, Pradeep Kumar Chennai India Vellore Institute of Technology Centre for Advanced Data Science Tamil Nadu Chennai India Yonsei University Department of Computer Science 50 Yonsei-ro Sudaemoon-gu Seoul03722 Korea Republic of Kiit University School of Computer Engineeing Odisha India

ISBN: (纸本)9798350349856

Businesses are increasingly concerned about insider threats, which highlights the need for effective mitigation strategies. This paper examines the variety of insider threats and provides a thorough plan for lowering this risk. Examining different internal threat types and their motives may help organizations identify any possible weak points. This paper also looks at technological advancements like user behavior analytics and anomaly detection as well as organizational practices like security culture promotion and employee training. It is advised to create a comprehensive framework for preventing insider threats that incorporates technology controls, organizational procedures, and human factors. In the implementation, a Windows application developed with Flutter and Dart takes user-input IP addresses to modify a Python script file using file handling. It also handles file name and path changes in the script, converts it into an executable file, and stores it with a data file. The application includes pages for inputting IP addresses, selecting files, and protecting the script. After protection, the executable file is saved in an executable folder, which can be further converted into a steganography file. This research focuses on evaluating the file's behaviour on different networks to enhance security. © 2024 IEEE.

关键词： Anomaly detection

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A Machine Learning Framework for Drug-Drug Interaction Prediction Using Smiles Features

A Machine Learning Framework for Drug-Drug Interaction Predi...

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International Conference on Web Research (ICWR)

作者： Mahdi Nouroozi Fatemeh Nasiri Mohsen Hooshmand Dept. Computer Science Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Predicting drug-drug interactions in disease treat- ments is crucial. Identifying these interactions can significantly impact the effectiveness and safety of a treatment. Efforts have been focused on utilizing knowledge about drug-drug interactions. However, not all interactions are known. This study presents machine learning-based methods - KNN, logistic regression, SVM, random forest, XGBoost, and a convolution neural network - to predict drug-drug interactions. We improved the dataset using the DrugBank database and proposed a learning framework for efficient prediction. The results demonstrate that the proposed framework performs well, especially when using SVM and logistic regression as the learning engines.

关键词： Support vector machines Logistic regression Predictive models Nearest neighbor methods Fingerprint recognition Transformers Feature extraction Data models Chemicals Context modeling

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Applying a Hybrid Deep Learning Framework to Efficient Stock Price Prediction 4

Applying a Hybrid Deep Learning Framework to Efficient Stock...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Khatri, Eti Ramesh Babu, Ch. Nandhini, I. Reddy, Sowmya Kethi Yoges, G. Reddy, Pundru Chandra Shaker Nitte Meenakshi Institute of Technology Department of Management Studies Bengaluru India Department of Computer Science and Engineering Hyderabad India V.S.B.Engineering College Department of Information Technology Karur India School of Management Studies Chaitanya Bharathi Institute of Technology Hyderabad India Maharaja Engineering College Avinashi India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350352931

It is very difficult to make reliable financial predictions using stock market data due to its inherent volatility and non-linearity, especially in the period following a pandemic. In response to these difficulties, this study builds a hybrid framework using deep learning to forecast technology stock prices, financial trends, and risk quantification. An increasing number of individuals have started investing in the stock market due to the fast growth of the economy in the past few years. A novel stock market prediction based on deep learning (SMP-DL) is presented in this research as a method for predicting stock market values. This study forecasted the stock market closing price using a bidirectional gated recurrent unit (BiGRU) in conjunction with long short-term memory (LSTM). When tested against other systems currently in use, the results demonstrated that the suggested system performed admirably. Since the values of R2, RMSE, MDA, MAE, and 0.8, respectively, are 3.4, 6.7, and 3.8. © 2024 IEEE.

关键词： Long short-term memory

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advanced DHCP Strategies for Seamless Inter-Campus Connectivity in Educational Networks 8

Advanced DHCP Strategies for Seamless Inter-Campus Connectiv...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Patadia, Chintan Patel, Akash Gandhi, Moksha Patel, Sachin Jagadeesh, Namratha Thirupatthi, Thanesh Nagarajan Lakehead University Computer Science Thunder Bay Canada Charotar University of Science and Technology Devang Patel Institute of Advanced Technology and Research Faculty of Technlogy and Engineering Department of Information and Technology Changa India

ISBN: (纸本)9798350376425

In today's educational landscape, resource sharing across different sites is crucial. This research study explores the implementation of Dynamic Host Configuration Protocol (DHCP) relay agents to enhance inter-campus connectivity in a large educational institution. By strategically placing and configuring DHCP relay agents, the study demonstrates their effectiveness in facilitating communication across dispersed network segments. This improves data transfer efficiency and reduces latency, enhancing overall network performance. This research study provides valuable insights for network administrators and stakeholders, offering practical guidance on configuring DHCP relay agents and optimizing network architecture. By implementing these recommendations, educational institutions can establish a scalable, reliable, and user-friendly network infrastructure. © 2024 IEEE.

关键词： Internet protocols

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Melanoma Detection using advanced Deep Neural Network 4

Melanoma Detection using Advanced Deep Neural Network

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4th International Conference on Energy, Power, and Environment, ICEPE 2022

作者： Sharma, Pallabi Gautam, Anmol Nayak, Rajashree Balabantaray, Bunil Kumar National Institute of Technology Meghalaya Computer Science and Engineering Shillong India NIT Meghalaya Computer Science and Engineering Shillong India JIS Institute of Advanced Studies and Research Center for Data Science Kolkata India

ISBN: (纸本)9781665471244

Melanoma is a type of skin cancer that starts in the cells (melanocytes) that govern the color of your skin. Melanoma is the most lethal one among all other skin diseases and the only reason for 77% deaths due to skin cancer. The best way to reduce these deaths is to detect cancer at its early stages so it can be treated and cured with minor treatment or surgeries. To speed up and improve the process of early detection, we propose an automatic classification method for melanoma cancer using an advanced deep neural network. Deep learning models require a large dataset to work efficiently, but due to limited time and the heavy workload of doctors, there is a lack of annotated skin can-cer images. Therefore, the proposed model introduces adversarial training for achieving better accuracy even with a small amount of data. The model removes unnecessary details and noise from the image and amplifies the depth and gradient in the dimensions and the shade of the image. This proposed adversarial method uses the gradients of the loss with respect to the input image to create a new adversarial example image that maximizes the loss for an input image. The synthetically generated images are used in the classification system for training and testing purposes. A comparative analysis of training with an adversarial approach and without an adversarial approach on different pre-trained models, namely VGG16, VGG19, Densenet121, and Resnet101, is also introduced in this work. ResNet101 with adversarial training has shown a state-of-the-art accuracy performance of 84.77% for melanoma classification. Therefore, the proposed approach can be considered an efficient method for classifying benign and malignant melanoma. © 2022 IEEE.

关键词： Deep neural networks

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FedEnv-Net: Federated Neural Network for Decentralized Health Risk Prediction in Dynamic Urban Environments

FedEnv-Net: Federated Neural Network for Decentralized Healt...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Priyadharshini, M. Murugesh, V. Chowdhury, Subrata Dash, Bibhuti Bhsan Chandra De, Utpal Patra, Sudhansu Shekhar Department of Computer Science and Engineering Hyderabad501 203 India Koneru Lakshmaiah Education Foundation Department of Cse AP Vaddeswaram India Sreenivasa Institute of Technology and Management Studies Department of Cse Anthra Pradesh Chittoor India Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798331508685

The predictions of health risks in urban settings are constrained by factors such as pollution and temperature which change over time. Centralized models have problems with privacy and scalability that often do not allow implementing solutions in real-world large, and distributed systems. To this end, we introduce the FedEnv-Net which is a federated neural network to predict health risk in a decentralized manner. Using federated learning, FedEnv-Net train's models directly on edge devices, where data is trained and only updates are shared. Dynamics of temporal patterns and features are predicted by LSTM branch and Dense branch respectively in the model. A Meta-Learner merges such branches for prediction and a tuned loss function for conservative health risk estimations. Although it ensures privacy of data, FedEnv-Net yields an R2 score of 0.92 and an MAE of 0.14, which determines its predictive capability. Future work includes increasing the efficiency of the communication between the system components, broadening the number of input data sources and improving the level of privacy to make FedEnv-Net a versatile, privacy-preserving solution for real-time health risk assessment. © 2025 IEEE.

关键词： Decentralized Model Federated Learning Health Risk Prediction LSTM Privacy-Preserving Real-Time Monitoring Urban Health

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