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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Bernard Thomas Adityavikram Bhatt Shailendra Narayan Singh Department of Computer Science and Engineering ASET Amity University Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Humans are social and emotional beings. With the rapid normalization of work-from-home and hybrid work environments, most people are having a difficult time truly expressing their emotions, which when not addressed inevitably leads to drastic mental problems. In order to address or prevent these mental problems, detection and recognition of facial emotions serve as an initial step. This paper presents a facial emotion recognition model working on the CNN (convolutional neural network) architecture, trained and tested using the FER2013 dataset, achieving an accuracy of 74% of detecting seven emotions - Happy, Neutral, Fear, Sadness, Anger, Disgust, and Surprise.

关键词： computers Emotion recognition Accuracy Face recognition Computational modeling computer architecture Remote working Convolutional neural networks

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Image Denoising Framework Employing Auto Encoders for Image Reconstruction 1

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Jain, Shruti Bharti, Monika Jindal, Himanshu Department of Electronics and Communications Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India Department of Computer Science Engineering ASET Amity University Punjab Punjab Mohali140603 India

ISBN: (数字)9789819986460

ISBN: (纸本)9789819986453

Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent a (sparse) input dataset in a compressed form that retains the most relevant information such that it may be reconstructed at the output with minimal loss from the compressed representation. In this paper, deep AE, denoising AE, and variational AE are used. Any AE where an extra constraint is put on the bottleneck to have a low KL divergence from a Normal Distribution is a Variational AE. There are multiple ways in which Variational AE is used, but the most common one is generative. The decoders on the top of the bottleneck can be used to generate new data points. Maximum accuracy of 88.85% is observed using the Denoising autoencoder while 76.25% and 81.44% are observed for deep and Variational autoencoder, respectively. 8.3% and 14.18% accuracy improvement is observed in Variational and deep AE, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image reconstruction

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A Neuro-Fuzzy Approach to MOPP Open Time Prediction for Chemical Threats in Low-Intensity Conflicts

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International Journal of Fuzzy System Applications 2022年第1期11卷 1-17页

作者： Kumar, Vijay Tyagi, Lakshya Department of Applied Mathematics Amity Institute of Applied Sciences Amity University Noida India Department of Computer Science and Engineering ASET Amity University Noida India

CBRN (chemical, biological, radiological, and nuclear) weapons systems hold the potential to cause great damage to a fighting unit. Chemical warfare agents are cheaper and easier to produce and deploy. Mission-oriented protective posture (MOPP) equipment is used to isolate soldiers from the toxic environment in case of a chemical attack. Sustained wearing of MOPP overgarments can put soldiers at the risk of dehydration and heat stress, while reducing their capability to carry out labour-intensive tasks efficiently. A hierarchal adaptive neuro-fuzzy inference system (HANFIS) model is proposed to predict worst case MOPP open times for surfaces covered with chemical agent resistant coating (CARC). The model can provide decision support for commanders when determining MOPP open or unmasking time. From the HANFIS simulation results, it was concluded that the model can accurately predict MOPP open times according to data present in military field manuals in a manner that is computationally efficient and easy to interpret based on linguistic rules, and most importantly, easy to customize according to the needs of in-theatre commander based on intelligence on the enemy's arsenal of CW agents and weather conditions of their unit's location. This work adds to the body of evidence that soft computing techniques can be effectively applied to the design of decision support systems for defence sciences. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

关键词： Chemical warfare

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Towards Smart Waste Sorting: Lightweight CNN with Attention Mechanisms for Enhanced Classification

Towards Smart Waste Sorting: Lightweight CNN with Attention ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Rashi Chauhan Abhishek Singhal Aakanksha Kaushal Prakhar Gupta Computer Science and Engineering ASET Amity University Noida Uttar Pradesh India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Managing the growing challenges of municipal solid waste is crucial for environmental sustainability, and effective waste management plays a key role in achieving that goal. One promising solution is automating the sorting of refuse, which can greatly improve recycling and overall waste management. However, many current deep learning models that rely on transfer learning suffer from high computational demands and limited adaptability in resource-constrained environments. In this work, we introduce a lightweight convolutional neural network (CNN) specifically designed to overcome these limitations in trash classification. The model incorporates advanced attention mechanism, namely Squeeze-and-Excitation (SE) and Spatial Attention (SA) blocks along with the Mish activation function, collectively augmenting feature representation and optimizing gradient flow. Evaluated on the Kaggle Garbage Dataset, our proposed model achieves a very high classification accuracy of 96.54%, far beyond conventional transfer learning models including VGG16, ResNet50, and EfficientNet B0. With just over 0.8 million parameters and an inference time of only 15 milliseconds per image, the model demonstrates both high computational efficiency and excellent scalability. These results highlight the model’s potential for real-time deployment in resource-limited settings, offering a sustainable and effective alternative for automated waste management. Ultimately, our approach promises to improve garbage classification systems and support more environmentally friendly waste management strategies by leveraging more efficient tools.

关键词： Waste management Waste materials Adaptation models Attention mechanisms Computational modeling Scalability Transfer learning Convolutional neural networks Sorting Residual neural networks

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Comparison of Affiliate Programs Strategies Using Different Platforms 2

Comparison of Affiliate Programs Strategies Using Different ...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kumar, Ravi Kumar, Kapil Tomar, Ankit Jain, Paras Goel, Lipika Gupta, Vishan Kumar COER University Computer Science & Engineering Roorkee India Graphic Era Deemed to be University Computer Science & Engineering Dehradun India VIT Bhopal University Kothri Kalan SCEAI MP Bhopal India Sridevi Women's Engineering College Department of AIML Hyderabad India Amity University Punjab ASET Punjab Mohali India

ISBN: (纸本)9798331519056

This research about the specific objectives of given affiliate programs will involve comparison of the significant aspects that include commission plans, program specifics, marketing tools, payment options, as well as affiliate assistance. This abstract provides an overview of a comparative analysis of affiliate program strategies employed by five prominent platforms:, some of the most popular affiliating programs that are available include the amazon associates, eBay partner network, Walmart associates, Rakuten affiliate, CJ affiliate and ShareASale. Primary and secondary data sources are used to gather information on the official website, and terms and conditions of programs and industry reports. Since a number of values would be numeric, for example, commission rate, cookie duration, and payout threshold, statistical methods will be employed;program features and other promotional tools will be assessed through content analysis. Besides, this research will help fill the gap in the literature on affiliate marketing strategies, examining the development and directions of this type of activity. © 2024 IEEE.

关键词： Commerce

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GreenGuard: Advanced Sensor Solutions for Enhanced Crop Health and Growth

GreenGuard: Advanced Sensor Solutions for Enhanced Crop Heal...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Tyagi, Ishaan Gupta, Rajat Upadhyay, Divya Dubey, Ashwani Kumar Sharma, Puneet ABES Engineering College Ghaziabad India Amity University Uttar Pradesh ASET UP Noida India SR University School of Computer Science & Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350385199

This study investigates an innovative soil management approach integrating advanced sensor technology to enhance crop growth conditions. The research focuses on critical soil parameters: pH, moisture, and temperature, essential for plant health and soil quality. By employing state-of-the-art sensors, the study continuously monitors and adjusts these parameters, promoting sustainable agriculture and increased crop yields. features include real-time tracking of soil pH, providing insights into nutrient availability and microbial activity, crucial for soil health. Moisture sensors accurately measure soil water content, aiding in precise irrigation practices and preventing harmful effects of improper watering. Additionally, temperature sensors help maintain ideal conditions for microbial activity and root development. The research includes extensive experimental work to evaluate the effectiveness of this sensor-based system in enhancing plant growth and soil fertility. The results demonstrate the potential of this technology-driven approach to boost agricultural output while minimizing environmental impacts. A notable aspect of the system is an alert mechanism that informs users about significant changes in soil conditions, aiding timely crop management interventions. The paper contributes to the scientific understanding of plant-soil relationships and offers practical strategies for agricultural practitioners. It presents a comprehensive, data-centric method combining pH, moisture, and temperature sensors, paving the way for a sustainable and efficient future in agriculture. © 2024 IEEE.

关键词： Smart agriculture

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Diabetes Estimation Through Data Mining Using Optimization, Clustering, and Secure Cloud Storage Strategies

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SN computer science 2024年第6期5卷 781页

作者： Gupta, Shyam S. Pandey, Tushar Kumar Raju, Vadali Pitchi Shrivastava, Rajeev Pandey, Rajeev Nigam, Ankita Roy, Vandana Department of Computer Science and Engineering Amity School of Engineering and Technology (ASET) Amity University College of Community Science Central Agricultural University Indur Institute of Engineering & amp Technology Princeton Institute of Engineering & amp Technology for Women Hyderabad DoCSE UIT-RGPV Department of CSE Princeton Institute of Engineering and Technology for Women Hyderabad Department of EC Gyan Ganga Institute of Technology and Sciences

Asian Indians face a unique risk for type 2 diabetes, with earlier onset and diagnosis at lower BMIs than other populations. Their susceptibility to diabetes-related health risks is higher compared to white populations. India struggles with low public awareness, a shortage of healthcare professionals, and limited treatment accessibility. Innovative strategies using available technological resources could significantly improve diabetes management in India. These challenges, common in developing countries, suggest that insights from India’s experience could benefit global diabetes management. This study aims to develop a hybrid model for predicting type 2 diabetes mellitus (T2-DM) to enhance awareness and preventive strategies. Using the Pima Indian Diabetes Dataset, the model’s effectiveness is validated through various performance metrics. The Fuzzy C-Means (FCM) clustering method is introduced, outperforming traditional models by creating more cohesive clusters with fewer variables. To enhance FCM's effectiveness, the Particle Swarm Optimization (PSO) technique is integrated, preserving FCM's strengths while improving efficiency. The hybrid model's sensitivity is 5.85% higher than K-Means + C4.5, and specificity is 2.79% higher. The proposed model achieves greater accuracy (96.58%) than previous methods. This approach aims to improve diabetes care in India and offers a model adaptable worldwide, addressing a critical global health challenge. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BMI Data mining Diabetic retinopathy FCM PSO T1-DM T2DM

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Image Recognition and Classification using CNN: A Review

Image Recognition and Classification using CNN: A Review

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Maroofa Yaseen Shailendra Narayan Singh Department of Computer Science and Engineering ASET Amity University Noida Uttar Pradesh India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This review paper has examined the field of foggy image detection and defogging algorithms using convolutional neural networks to handle the challenges and limitations of poor visibility during harsh weather conditions such as fog. A detailed comparison of three of the most successful fog detection algorithms—FogNet, FogCNN, and DeepFog—based on a set of performance indicators including precision, recall, F1 score, false positive rate (FPR), false negative rate (FNR), and the area under the receiver operating characteristic curve (AUC-ROC) has been conducted. From the performance evaluation, it is clear that DeepFog offers the best performance in distinguishing between foggy and non-foggy images with higher levels of accuracy. In addition, we have surveyed various imaging techniques with several methodologies that outperformed previously published defogging algorithms based on metrics such as Structural Similarity Index and Peak Signal-to-Noise Ratio. Moreover, we also consider the future development of such technology through multi-modality fusion strategies, real-time optimization, adversarial training, and human-centric applications to give an impression of the advancement of robust systems for these real-world use scenarios.

关键词： Training Performance evaluation PSNR Reviews Neural networks Receivers Real-time systems Convolutional neural networks Optimization Meteorology

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A Despeckling Filter for Ultrasound Images Based on Cellular Automata Approach

A Despeckling Filter for Ultrasound Images Based on Cellular...

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International Conference on Intelligent Manufacturing and Energy Sustainability, ICIMES 2021

作者： Bhardwaj, Ankur Kaur, Sanmukh Shukla, Manoj Shukla, Anand Prakash Computer Science and Engineering Department ASET Amity University Noida India Electronics and Communication Engineering Department Amity University Noida India Department of Computer Science and Engineering KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789811664816

In the medical profession, ultrasound imaging for clinical recognition is far better suggested than any other imaging mode because of its adaptability, suitability, safety, and viability. The images that are taken and then preprocessed as part of the procedure are crucial in medical diagnosis. Different types of noise can be added to the images during the uploading process. The more deteriorated the image, the more difficult it is to better isolate the required components, recognize them, and examine edge clarity. In this study, a comparison is made between several filters such as Gaussian, Kuan, Lee, guided, and anisotropic filters, as well as the proposed filter, which is based on the cellular automata principle. Developing the proposed filter using the cellular automata approach yields superior results in terms of image parameters accessible for consideration. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cellular automata

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Incorporating Requirement engineering into Agile Methodologies: Challenges and Proposed Solutions 9

Incorporating Requirement Engineering into Agile Methodologi...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Srivastava, Prateek Srivastava, Nidhi Agarwal, Rashi Singh, Pawan Amity Institute of Information Technology Amity University Uttar Pradesh Lucknow Campus Lucknow India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur India Department of Computer Science and Engineering ASET Amity University Uttar Pradesh Lucknow Campus Lucknow India

ISBN: (纸本)9798350383201

Agile software development has a good track record, but only when the project size is limited. Requirement engineering (RE) is critical because "Requirements" play a critical role at each stage of the lifecycle of software. Agile supports changing customer requirements while adding value to the business, it also comes with its own set of obstacles, many of which are connected to requirement constraints. This study seeks to uncover the issues that agile project requirements engineers confront. Many research studies on requirement issues have been undertaken, all of which are compromised in some way, offer no suggestions for improving the agile development process, and fail to reveal massive agile development hurdles. This paper addresses all the issues raised above and suggests possible solutions from the standpoint of requirement engineering and results can be extremely beneficial to the software business in terms of improving development processes, as well as individuals who would like to continue working in this area. © 2023 IEEE.

关键词： Life cycle

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