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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Rajasekhar, Darani Rafi, D. Mahammad Chandre, Shanker Kate, Vandana Prasad, Jhakeshwar Gopatoti, Anandbabu Department of Electronics and Communication Engineering Andhra Pradesh Kurnool India Gitam University Department of CSE Telangana Hyderabad India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal India Acropolis Institute of Technology and Research Department of Computer Science and Information Technology Madhya Pradesh Indore India Shri Shankaracharya College of Pharmaceutical Sciences Chhattisgarh Bhilai India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350346640

New statistics show that about 950,000 individuals a year lose their lives to breast carcinoma, making it the deadliest form of cancer worldwide. Early detection and accurate diagnosis of an illness can increase the likelihood of a favorable result, decreasing the mortality rate. Premature victims can be spared if the disease is detected early. Investigators who study cancer face various challenges, including the difficulty of differentiating benign and malignant tumors and the difficulty of classifying mild and metastatic breast cancer. Here, the pattern-recognition algorithms are used to accurately identify every tumor. However, they are all based on the idea of "binary grouping"(malignant and benign). This research made use of different pre-trained networks to speed up the training process. While evaluating the efficacy of the models, many measures were used. By using the images captured during the modeling process, both breasts can be shown together, without having to split them to show the right and left sides. After the data preparation phases, the Ensemble learning model showed the highest classification performance with an accuracy value of 100% when compared to the other pre-trained network models. In this research, thermographic images of breast cancer were used to classify the disease, and the results were combined with experimental data to form a computer-aided diagnosis method. Clinical data is used to validate the models' predictions. After constructing and evaluating two models with distinct designs, the model using the same design performed best, suggesting that the clinical data decisions were essential in improving the model's performance. © 2023 IEEE.

关键词： Diseases

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Design and Develop an Efficient Load Balancing Approach with AOMDV Routing in 7G-FANET 4

Design and Develop an Efficient Load Balancing Approach with...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Soni, Gaurav Chandravanshi, Kamlesh Deoskar, Devashri Parashar, Sadhana Thakur, Ruchi Vit Bhopal University School of Computing Science and Engineering Kothrikalan Bhopal-Indore Highway Madhya Pradesh Sehore India Lakshmi Narain College of Technology Dept. of Computer Science and Engineering Raisen Road Madhya Pradesh Bhopal India Bansal Institute of Research and Technology Dept. of Computer Science and Engineering Kokta Bypass Rd Anandnagar Madhya Pradesh Bhopal India Sati Dept. of Computer Science and Engineering Madhya Pradesh Vidisha India

ISBN: (纸本)9798331518981

The congestion in the Flying Ad hoc Network (FANET) is due to insufficient bandwidth or a heavy load on the network. The multipath routing protocol AOMDV (Ad -hoc On-demand Distance Vector) is used for balance the load in network. The multiple routes serve as backup routes for data transmission in the connection breakdown. The speed of the Seventh Generation (7G) is much higher than other generations of wireless networks. 7G has a speed of 100 Gigabits, which reduces communication latency. To optimize the bandwidth capacity, we must shift the load to other or alternative routes for handle congestion and proper load balancing. In this paper, propose a delay estimation rate-based control scheme to improve UAV-to-UAV communication in 7G-FANET. The Efficient Load Balancing AOMDV (ELAOMDV) scheme furnishes data on the number of packets that failed to reach their destination. The variation in the rate of transmission is based on the delay of successful acknowledgement. The proposed approach is to avoid the risk of congestion and balance the load on a specific link. 7G has the advantage of rapid communication. The ELAOMDV congestion management strategy is to minimize the latency and buffer overflow caused by network congestion. The delay difference confirms the existence of congestion and indicates the availability of multipath to balance the load. The performance of ELAOMDV is better than the multipath ad hoc on demand protocol (MAODV). All of the performance metrics showing improved results, indicating that the ELAOMDV approach has improved routing performance in 7G-FANET. © 2024 IEEE.

关键词： Bandwidth

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Leveraging Blockchain for Eco-Innovation: Adaptive Deep Learning Model Driving Sustainable Green Future 7th

Leveraging Blockchain for Eco-Innovation: Adaptive Deep Lear...

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Karale, Ankita Rajawat, Anand Singh Goyal, S.B. Solanki, Ram Kumar Rane, Pinky Zhu, Liu Chee, Weiming Sandip Institute of Technology and Research Centre Nashik India School of Computer Science & Engineering Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore453331 India City University Petaling Jaya46100 Malaysia

ISBN: (纸本)9783031733239

The sense of urgency in addressing environmental challenges has sparked a shift towards adopting sustainable practices in various industries. While blockchain technology is commonly associated with cryptocurrency, it possesses the potential to bring about transformative changes beyond financial transactions, particularly in the promotion of eco-innovation. This research paper explores the integration of adaptive deep learning models with blockchain technology, aiming to advance the cause of a sustainable and environmentally friendly future. We propose a novel approach in which the transparency and immutability inherent in blockchain are combined with the predictive and analytic capabilities of adaptive deep learning. This collaboration enables the efficient monitoring of environmental metrics, facilitating green supply chain management, waste reduction, and optimizing energy consumption. Moreover, our model introduces incentives for sustainable practices through tokenization, creating a participatory green economy. Case studies provide evidence of how this integrated system enables real-time decision-making, adaptation to environmental changes, and ensures accountability. Through this research, we envision a paradigm in which technology not only supports economic growth but also upholds the principles of environmental stewardship, thereby paving the way for a balanced and sustainable future. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decentralized finance

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： Nitika Singhi Ankush Verma Atul Agrawal Irfan Khan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management & Research Indore India Computer Science Engineering Sushila Devi bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS.

关键词： Technological innovation Solution design Heuristic algorithms Design methodology Brain modeling Real-time systems Iterative methods

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Experimental Investigation for Hybrid Energy Harvesting System for Remote Monitoring Applications 1

Experimental Investigation for Hybrid Energy Harvesting Syst...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tripathi, Abhishek Deena, A.C. Arvinthan, T.R. Vijayaragavan, G. Anjankar, Shubham Tiwari, Subhashish Soni, Gireesh G Kalasalingam Academy of Research and Education Department of Computer Science Engineering Tamil Nadu India Kalasalingam Academy of Research and Education Tamil Nadu India Shri Ramdeobaba College of Engineering and Management Department of Electronics Engineering Nagpur India GITAM University Department of Electronics Engineering Bangalore India Shri G S Institute of Technology and Science Indore India

ISBN: (纸本)9798350353648

This study addresses the demand for sustainable energy systems in remote monitoring scenarios, particularly for Wireless Sensor Networks and IoT apparatuses. It introduces a hybrid approach to energy harvesting that utilizes both solar power and kinetic energy from piezoelectric sources, aiming to develop dependable and efficient power solutions that support extended operations in isolated settings. By integrating the solar and piezoelectric technologies, the proposed system ensures a continuous energy supply, crucial for the uninterrupted functionality of remote sensors. The implementation of ESP8266 modules facilitates WiFi connectivity, allowing data transmission to ThingSpeak platforms for effective data visualization and analysis. This configuration not only enhances the capability for real-time remote monitoring but also maximizes the operational lifespan of the sensor network without the need for frequent maintenance or battery replacements, thereby offering a significant advancement in the sustainability of IoT deployments. © 2024 IEEE.

关键词： Solar energy

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Natural Language Processing for Generating Synthetic News Detection 7

Natural Language Processing for Generating Synthetic News De...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Sharan, Bediga Mulani, Umar Mubarak Naveenkumar, R. Badodia, Sujit Kumar Makawana, Hemant Dhanraj, Joshuva Arockia Sr University School of Computer Science & Artificial Intelligence Telangana Warangal India Kj College of Engineering and Management Research Maharashtra Pune India Chandigarh Colleges of Engineering Chandigarh Group of Colleges Department of Computer Science and Engineering Jhanjeri Punjab Mohali India Institute of Engineering and Technology Devi Ahilya Vishvavidyalaya Madhya Pradesh Indore India Devarakaggalahalli Karnataka Bengaluru India

ISBN: (纸本)9798350350067

Synthetic news production, language translation, and text summarization have all seen significant success with sophisticated text generating techniques. Although neural model-generated false news detection has advanced recently, the results are not easily transferable to the efficient identification of misinformation created by humans. The significant contrasts between fake news produced by machines and those written by humans, including style and underlying intentions, are what prevents them from successfully transferring. In light of this, we provide a brand-new framework for creating training cases that draw inspiration from the well-known techniques and styles of propaganda written by people. But these methods might be misused to spread false information. researchers create superior news material using a unique generation technique called FACTGEN in order to gain a greater understanding the possible risks associated with synthetic news. Because most text generation techniques now in use either provide very little more information or break continuity across the inputs and outputs, the synthetic news becomes less reliable. The results of experiments conducted on real-world datasets show that FACTGEN generates news items that are bursting with data and accurate. © 2024 IEEE.

关键词： Translation (languages)

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An innovation analysis of Machine Learning model to Automate Network Anomaly Detection through Time Series Analysis 15

An innovation analysis of Machine Learning model to Automate...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Rongali, Aruna Sri Qureshi, Anjum Nazir Upadhyay, Jyoti Lavanya, P. Matad, Nanditha S Jain, Sanjiv Kumar University of The Cumberlands Department of Information Technology United States Rajiv Gandhi College of Engineering Research and Technology Department of Electronics and Communication Chandrapur India Gd Rungta College of Science and Technology Department of Computer Science Chhattisgarh Bhilai India Institute of Aeronautical Engineering Hyderabad India New Horizon College Department of Mathematics Bangalore India Med-Caps University Department of Electrical Engineering Indore India

ISBN: (纸本)9798350370249

This paper explores the potential of machine-mastering models to automate network Anomaly Detection (NAD) through Time series analysis. We employ a two-level method wherein the primary degree entails function selection thru foremost component analysis (PCA), accompanied by gadget mastering (ML) model choice from more than a few supervised studying algorithms. The second stage evaluates the overall performance of the numerous selected ML models and optimizes theirhyperparameters when necessary. Our experiments demonstrate that ML-driven computerized network Anomaly Detection can provide accurate and well-timed detection of network anomalies with little supervision and parameter tuning attempts. The outcomes of our experiments display that Random Forests and Support Vector Machines (SVMs) carry out first-rate some of the model's grid searches, demonstrating aggressive accuracy and precision ratings from an anomaly detection perspective. We also intensely evaluate the consequences and provide insightful discussion on the possibilities and challenges surrounding using ML for automatic community Anomaly Detection. © 2024 IEEE.

关键词： Time series

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DETR-DC5 Approaches for Improved Spatial Object Detection in Satellite Imagery

DETR-DC5 Approaches for Improved Spatial Object Detection in...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Baskar, Radhika Dwibedi, Rajat Kumar Kumbhkar, Makhan Sah, Swati Patil, Harshal Firos, A. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Aarupadai Veedu Institute of Technology Vmrf Chennai India Indian Institute of Soybean Research Department of Computer Application Indore India School of Set Sharda University Greater Noida India Computer Science and Engineering Pune India Department of Computer Science and Engineering Rono-Hills Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350394580

Satellite spatial object detection is crucial for urban planning, environmental monitoring, and catastrophe management. The DETR-DC5 (Data Efficient Transformer with DC5 Backbone) is a cutting-edge object detection model. This study introduces DETR-DC5 framework-based satellite spatial object detection improvements. DETR-DC5's strengths are used to improve feature extraction and post-processing. First, a specific feature augmentation technique extracts richer and more discriminative spatial features to let the model capture subtle object attributes. Second, a semi-supervised learning training technique is used to address the lack of labeled satellite imagery data and improve the model's generalization. A new satellite imagery post-processing refining method uses non-maximum suppression methods. This refinement stage reduces false positives and improves localization, especially in densely populated metropolitan regions and complicated natural surroundings. On benchmark satellite imagery datasets, the suggested methods outperform baseline DETR-DC5 models in spatial object detection. Experimental findings indicate significant improvements in precision, recall, and F1-score, proving the upgrades work. This study uses the DETR-DC5 architecture, targeted feature augmentation, semi-supervised training, and refined post-processing algorithms to recognize spatial objects in satellite data. These techniques have the potential to dramatically improve satellite-based object detection, which has implications for urban planning, environmental monitoring, and disaster response. © 2023 IEEE.

关键词： DETR-DC5 Feature Augmentation Satellite Imagery Semi-Supervised Learning Spatial Object Detection

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Facial Expression Recognition Using Deep Learning Model on Crowd

Facial Expression Recognition Using Deep Learning Model on C...

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

作者： Prashant Shakyawar Upendra Singh Department of Computer Science and Engg. Prestige Institute of Engineering Management & Research Indore India Department of Information Technology Shri G. S. Institute of Technology and Science Indore India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

In the field of computer vision, Facial Expression Recognition (FER) is an essential problem that automates emotion analyzing and understanding in human facial expressions. Facial occlusions, changing lighting conditions and overlapping facial attributes in crowded environments make it difficult to recognize the expression of emotions. In this research we are solving a problem of group friendly facial expression recognition using deep neural network models. We also tested a number of complex deep neural network architectures to learn fine-grained patterns and representations in human face videos or images. In training and testing these models, resources such as AffectNet which encompasses a broad spectrum of people with various facial expressions is utilized to make them robust against most general cases in crowds. The results of the deep neural network model are presented, revealing its ability to detect facial expressions efficiently with high accuracy and recall. This is supported by the performance of their models being around 80–94 % in terms of accuracy and F1-score, based on different studies. Their benefits involve deploying state-of-the-art architectures and performing robustly on large scale datasets; fusing multi-modal for improved recognition, learning temporal relationships will be well established technologies; scalability to resource-constrained environments.

关键词： Training Emotion recognition computer vision Accuracy Face recognition Computational modeling Artificial neural networks computer architecture Videos Testing

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Handwritten digit recognition using machine and deep learning algorithms

arXiv

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arXiv 2021年

作者： Dixit, Ritik Kushwah, Rishika Pashine, Samay Computer Science and Engineering Acropolis Institute of Technology and Research Indore India

The reliance of humans over machines has never been so high such that from object classification in photographs to adding sound to silent movies everything can be performed with the help of deep learning and machine learning algorithms. Likewise, Handwritten text recognition is one of the significant areas of research and development with a streaming number of possibilities that could be attained. Handwriting recognition (HWR), also known as Handwritten Text Recognition (HTR), is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices [1]. Apparently, in this paper, we have performed handwritten digit recognition with the help of MNIST datasets using Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and Convolution Neural Network (CNN) models. Our main objective is to compare the accuracy of the models stated above along with their execution time to get the best possible model for digit recognition. © 2021, CC0.

关键词： Support vector machines

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