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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Siddharth Joshi Vandana Kate Shilpa Bhalerao Prasoon Tripathi Sudhanshu Sharma Dept. of Computer Science and Information Technology Acropolis Institute of Technology and Research Indore India

The title “Samarjini” draws its inspiration from the Sanskrit word for a cleaning tool, and this research paper introduces the development of an automatic mopper machine designed to streamline household cleaning tasks while maintaining affordability. In a market filled with high-cost alternatives, this IoT-based device stands out by offering autonomous navigation, obstacle detection and avoidance, and effective floor cleaning at a lower price. The paper explores the machine's design, hardware and software components, evaluates its performance in various scenarios, and underscores its relevance in today's fast-paced lifestyles. Extensive experimentation was conducted to assess the machine's performance in diverse environments and scenarios. The outcomes affirm that “Samarjini” excels in floor cleaning, adeptly avoiding obstacles and autonomously navigating through spaces.

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The institute of Electronics and Information Engineers.

关键词： Demand response

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Towards analyzing mobile app characteristics for mobile software development 8

Towards analyzing mobile app characteristics for mobile soft...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Patidar, Anil Suman, Ugrasen Acropolis Institute of Technology Research Department of Computer Science and Engineering Indore India School of Computer Science Information Technology Devi Ahilya Vishwavidyalaya Indore India

ISBN: (纸本)9789380544434

A mobile application (i.e., mobile app) is a small software program, which is mainly developed for mobile phones. Mobile apps have some peculiar characteristics, which are concerned with particular aspects in the form of requirements, pertaining to hardware, software and network connectivity. Designing an appropriate mobile app according to users' perspective depends on the mobile app characteristics. In this paper, we present a review of various available mobile app characteristics concerning to present mobile apps. It is observed that the user experience is one of the most exciting and evolving feature that attracts the majority of mobile app users. In order to deal with user experience, we have further explored and found some essential factors that affect it. Furthermore, mobile app paradigms play an important role for mobile app development. Mobile app characteristics help to select a suitable paradigm for mobile apps development. We have analyzed that hybrid mobile app paradigm fits in most of the mobile app development situations. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Application programs

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The Evolution and Impact of Price Comparison Websites: A Technological and Economic Analysis

The Evolution and Impact of Price Comparison Websites: A Tec...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Mukesh Patidar Daxa Vekariya Preeti Pradhan Kartik Gupta Praveen Kumar Patidar Pirmohammad Khan Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India Department of ESH (Chemistry) Indore Institute of Science & Technology Indore Madhya Pradesh India Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore Madhya Pradesh India Department of Computer Science & Engineering Parul Institute of Technology Parul University Vadodara Gujarat India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Price comparison websites (PCWs) have become integral to the online shopping experience, enabling consumers to make informed decisions by comparing prices, features, and reviews from multiple retailers. These platforms not only benefit consumers by saving time and money but also influence market competition by increasing pricing transparency and driving businesses to optimize their strategies. This research explores the evolution of PCWs, their technological infrastructure, and their business models. Moreover, it examines their impact on consumer behavior, the challenges they pose for retailers, and the future trends shaping the industry. Through an analysis of current practices, technological innovations, and economic effects, this work highlights the growing importance of PCWs in the global e-commerce ecosystem.

关键词： Technological innovation Reviews Ecosystems Pricing Market research Real-time systems Electronic commerce Reliability Next generation networking Business

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An Extensive Analysis of Neuromorphic Computing

An Extensive Analysis of Neuromorphic Computing

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Amit Vajpayee Palak Preet Kaur Ankit Sharma Santosh Varshney Department of Computer Science and Engineering Parul Institute of Engineering and Technology Vadodara India Department of Bigdata Analytics Apex Institute of Technology Chandigarh University Mohali India Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Neuromorphic computing is a new paradigm that emerges from the structure and function of the human brain and aims to revolutionize computing. The technology is designed to simulate the high speed, low power consumption and large contraction of the brain. Neuromorphic systems provide great potential for intelligence and big data by using special algorithms and special designs to simulate neural networks. Despite encouraging progress from technology companies such as Intel and IBM, the development of neural computing chips still faces major challenges. This approach brings significant benefits to artificial intelligence, machine learning and big data. This technology is still in its infancy but has great potential to revolutionize intelligence. By mimicking the brain's performance and learning potential, neuromorphic computing could pave the way for smarter, faster, and more flexible systems that work well on water, performing tasks such as complex data analysis and on-the-fly decision making.

关键词： Technological innovation Machine learning algorithms Power demand Neuromorphic engineering Reviews Machine learning Big Data History Biological neural networks Faces

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Cognitive Computing's Role in Shaping the Future Landscape of Healthcare

Cognitive Computing's Role in Shaping the Future Landscape o...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Jain, Anunay Jain, Neelesh Verma, Ashish Jain, Anupama Shri G. S. Institute of Technology Department of Information Technology Indore India Trinity Institute of Technology and Research Department of Computer Science Engineering Bhopal India Dr. Harisingh Gour Central University School of Mathematical & Physical Sciences Madhya Pradesh Sagar India Sagar Institute of Research & Technology Department of Computer Science Engineering Bhopal India

ISBN: (纸本)9798350348460

As the healthcare industry undergoes a paradigm shift towards data-driven and patient-centric models, cognitive computing emerges as a transformative force. This research paper delves into the profound impact of cognitive computing on the future landscape of healthcare. Through an extensive review of literature, case studies and emerging trends, this paper explores the applications, benefits, challenges, and ethical considerations associated with integrating cognitive computing into healthcare practices. Drawing on interdisciplinary perspectives, we examine how cognitive computing enhances diagnostic precision, optimizes treatment plans, facilitates personalized medicine, and improves overall healthcare delivery. The paper also scrutinizes the ethical implications of leveraging cognitive computing in healthcare, considering issues such as data privacy, bias, and the human-machine interface. By synthesizing existing knowledge and proposing future research directions, this paper contributes to the ongoing discourse on the role of cognitive computing in shaping the trajectory of healthcare. © 2024 IEEE.

关键词： Cognitive systems Digital healthcare Healthcare system

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DEFAEK: Domain Effective Fast Adaptive Network for Face Anti-Spoofing

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Neural Networks 2023年 161卷 83-92页

作者： Lin, Jiun-Da Han, Yue-Hua Huang, Po-Han Tan, Julianne Chen, Jun-Cheng Tanveer, M. Hua, Kai-Lung Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Mathematics Indian Institute of Technology Indore Simrol453552 India

Existing deep learning based face anti-spoofing (FAS) or deepfake detection approaches usually rely on large-scale datasets and powerful networks with significant amount of parameters to achieve satisfactory performance. However, these make them resource-heavy and unsuitable for handheld devices. Moreover, they are limited by the types of spoof in the dataset they train on and require considerable training time. To produce a robust FAS model, they need large datasets covering the widest variety of predefined presentation attacks possible. Testing on new or unseen attacks or environments generally results in poor performance. Ideally, the FAS model should learn discriminative features that can generalize well even on unseen spoof types. In this paper, we propose a fast learning approach called Domain Effective Fast Adaptive nEt-worK (DEFAEK), a face anti-spoofing approach based on the optimization-based meta-learning paradigm that effectively and quickly adapts to new tasks. DEFAEK treats differences in an environment as domains and simulates multiple domain shifts during training. To further improve the effectiveness and efficiency of meta-learning, we adopt the metric learning in the inner loop update with careful sample selection. With extensive experiments on the challenging CelebA-Spoof and FaceForensics++ datasets, the evaluation results show that DEFAEK can learn cues independent of the environment with good generalization capability. In addition, the resulting model is lightweight following the design principle of modern lightweight network architecture and still generalizes well on unseen classes. In addition, we also demonstrate our model's capabilities by comparing the numbers of parameters, FLOPS, and model performance with other state-of-the-art methods. © 2023 Elsevier Ltd

关键词： Large dataset

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A Deep Learning Approach to Improving Patient Safety in Healthcare Using Real-Time Face Mask Detection

A Deep Learning Approach to Improving Patient Safety in Heal...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Mukesh Patidar Praveen Bhanodia Kamal Kumar Sethi Shivshankar Rajput Pooja Sanjay Patil Chandrakant Tiwari Aayush Gid Kartik Gupta Department of Computer Science and Engineering PIET Parul University Vadodara Gujrat India CSE AITR Indore India Department of Computer Science and Engineering Acropolis Institute of Technology and Research (AITR) Indore India Credit acceptance Financial Institution in South field Michigan United States Department of Electronics and Communication Engineering Sri Aurobindo Institute of Technology Indore India Department of Electronics and Communication Engineering Indore Institute of Science and Technology Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Face masks are frequently used during health an emergency, which emphasizes the need for efficient mask detection systems to guarantee public safety and adherence to health laws. The goal of this research is to accurately identify between masked and unmasked human faces using a deep learning-based approach. By employing a blend of convolutional neural networks (CNNs), the research makes use of large-scale picture datasets to train the model and improve its performance in various scenarios. The model is a useful tool for automated surveillance and public health monitoring systems, as evidenced by experimental results that show its robustness in reaching high accuracy rates. The proposed work explore by using MobileN etV2 within a Python environment. The next stage in the process is pre-processing the data, which often entails normalizing pixel values and scaling images to the input size that MobileNetV2 expects (224×224 pixels, for example).

关键词： Measurement Deep learning Weight measurement Accuracy Computational modeling Surveillance Real-time systems Robustness Convolutional neural networks Streams

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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Advances in Computing research on science engineering and technology (ACROSET), International Conference on

作者： Dilip Kumar Sharma Aaditya Yadav Mukesh Patidar Pooja Sanjay Patil Praveen Kumar Patidar Devendra Singh Mandloi Rashid Sheikh Shivshankar Rajpoot Department of Electronics and Communication Engineering Ujjain Engineering College Ujjain India Department of Computer Science and Engineering PIET Parul University Vadodara India Credit acceptance Financial Institution in Southfield Michigan USA Department of Computer Science & Engineering Parul Institute of Technology Parul University Vadodara India Department of Electronics and Communication Engineering Indore Institute of Science and Technology Indore India Department of Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool.

关键词： Wireless sensor networks Energy consumption Protocols Clustering methods Machine learning Probabilistic logic Market research MATLAB

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District-Level Rainfall and Cloudburst Prediction Using XGBoost: A Machine Learning Approach for Early Warning Systems

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第2期49卷 375-396页

作者： Kumar, Guru Dayal Tyagi, Shekhar Pradhan, Kalandi Charan Shah, Akshat Migration and Development Research Group School of Humanities and Social Sciences Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Indian Institute of Technology Indore452020 India Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur440001 India

This research presents a novel methodology for cloudburst forecasting using the XGBoost (Extreme Gradient Boosting) machine learning algorithm. With the escalating impact of climate change, accurately predicting extreme weather events like cloudbursts is crucial due to their potential to trigger floods. Cloudburst events were identified from daily rainfall data. Our study leverages historical weather data, focusing on intricate rainfall patterns, to forecast future cloudburst occurrences. Comparative analysis against Random Forest and LSTM models confirmed XGBoost’s effectiveness, consistently outperforming alternatives across multiple performance metrics. The XGBoost model, known for its ability to handle complex datasets, demonstrated strong predictive performance, with an RMSE of 0.12 and an MAE of 0.09, indicating high accuracy. This research provides a reliable tool for advanced weather forecasting and early warning systems, offering valuable support to policymakers, disaster management teams, and agricultural planners in mitigating risks associated with extreme rainfall events. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Acid rain

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