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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Kottath, Anu V Malarvel, Muthukumaran Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350342796

The Monkeypox virus is a species of double stranded DNA virus that causes Monkeypox disease in humans and other mammals. The emergence of the Monkeypox virus poses a fresh threat to global health at a time, when the world continues to try to heal from the damage brought in by the widespread of COVID-19. Although the Monkeypox diseases are not that harmful or contagious as COVID-19, many new cases are reported from various nations across the world. As a result, it won't be shocking if there is another pandemic, because of insufficient precautions taken. Due to its resemblance to both chickenpox and measles, Monkeypox can be challenging to clinically diagnose in its initial stages. computer aided Monkeypox lesion detection may be useful for surveillance and the quick identification of suspected cases in circumstances where there is a difficulty in performing confirmatory Polymerase Chain Reaction (PCR) tests. Deep learning (DL) methods has shown enormous promise for image-based diagnosis including lung cancer detection, skin diseases detection, COVID-19 etc. Hence, the same application may be used to detect the Monkeypox diseases too. This paper evaluates four pre trained deep learning models (Squeeznet, DenseNet201, EfficientNetB0, ResNet50v2) for Monkeypox disease detection and six established metrics (Precision, Recall, Sensitivity, Specificity, F1-score and Accuracy) are used to analyze the results. This paper helps to identify the best pretrained model for Monkeypox diseases detection. A publicly available dataset is used in this work. © 2023 IEEE.

关键词： Chickenpox Deep Learning Machine learning Monkeypox Polymerase Chain Reaction (PCR)

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Detection of Non Helmet Riders and Extraction of Licence Plate Number

Detection of Non Helmet Riders and Extraction of Licence Pla...

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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Kumar, K. Ashok Dharshan Sekhar, Chandra Manikanta, S.D. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The current scenario of India's traffic laws poses a number of challenges, each of which may be addressed in a variety of ways. An increasing incidence of motorbike and moped-related fatalities have been linked to the common practice of driving without a headgear in India. The current system relies heavily on CCTV records to keep tabs on traffic offences, forcing cops to manually examine individual frames and, if the rider isn't wearing a helmet, focus in on their numberplate. However, this calls for a significant investment of time and resources, since traffic offences are common and the number of motorcyclists grows daily. What if there were a technology that could detect whether or not a motorcyclist was breaking the law by failing to wear a helmet and, if so, get the vehicle's registration number from the database? This study has been accomplished with recent success using HaaR, HoG, LBP, R-CNN, CNN features, etc. However, the pace, precision, and effectiveness of object identification and categorization are severely constrained by these works. In an effort to automate the process of finding instances of the traffic offense of not donning a headgear and retrieving the numberplate of vehicles, this study develops a Non-Helmet Driver identification method. The core idea is to use three tiers of DL for Image Classification. Individual, motor bike, headgear, and licence plate are all recognisedutilising YOLOv2's increasing levels of sophistication. Then, OCR is used to get the licence plate number. Particularly for the extraction of licence plate numbers, all of these methods are governed by a set of predetermined parameters and limits. Since moving images are used as input, the processing time is critical. By combining the aforementioned methods, we have developed a comprehensive system for identifying helmets and extracting licence plate numbers. © 2023 IEEE.

关键词： Extraction

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Deep Learning Model to Detect and Classify Bone Fracture in X-Ray ImagesBOOK_conf_196895_none_none_1of1_10394986

Deep Learning Model to Detect and Classify Bone Fracture in ...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Imrankhan, M. Varalakshmi, I. Arthi, J. Pavithra, P. Reshma, J. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350315127

The skeletal system of the human body is very important and bones are vital to it. A support and facilitate body movement. To detect fractures in bones, doctors use X-ray images, but this manual technique is laborious and prone to mistakes. Therefore, an automated approach for diagnosing bone fractures is required. Power electronic device modeling is a frequent use of deep neural networks, or DNNs. The proposed system, provides a deep neural network model using ResNet 50 to distinguish between bones that are healthy and damaged. But when faced with little data, our deep learning model has a tendency to over fit. The data augmentation techniques is used to increase the data set in order to overcome this problem. This stratergy uses 5-fold cross- validation and our model is able to discriminate objects with 92.44% classification accuracy. © 2023 IEEE.

关键词： Image classification

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Enhancing Crop Yields with Smart Agriculture Systems using Solar Trackers 2

Enhancing Crop Yields with Smart Agriculture Systems using S...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Ingale, Kiran Vidhale, Arpit Vikhar, Shivani Wagh, Ishita Waghmare, Akash Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350308846

Smart irrigation systems have become an important tool in modern agriculture to improve crop yields and conserve water resources. However, these systems often rely on electricity, which can be expensive and unreliable in remote or rural areas. To address this issue, this research paper proposes the use of a solar tracking system to maximize the efficiency of smart irrigation systems. The research paper presents the design and implementation of a smart irrigation system with a solar tracking system in a greenhouse environment. The system was tested using different irrigation strategies and solar panel orientations to determine the optimal configuration for maximizing efficiency. It demonstrates the potential of using a solar tracking system to maximize the efficiency of smart irrigation systems. The proposed system can be easily replicated and scaled up for use in different agricultural settings, providing an eco-friendly and costeffective solution for irrigation management. © 2023 IEEE.

关键词： Arduino UNO DHT 11 LDR Node MCU Solar Irradiance STSs

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Development of Miniature Wearable Ultra-Wideband Antenna Using Qualified Analysis of Different Substrate Material for BCC

Development of Miniature Wearable Ultra-Wideband Antenna Usi...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Chimanna, Mohan A Dhumal, Priyanka S Sagede, Manisha J Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350398465

In this paper we introduced an advance portable device using reliable wireless technology to trigger the communication between people and its nearby equipment's. Due to modernization now a days these electronic devices require the compatible miniature antenna. The compact size, low cost, better efficiency is the key requirement of the current research. This research work basically focused on the ultra-w notched type ultra-wide band antenna and is simulated using different substrate mounted on a ground which is generally some part of the ground surface. A rectangular substrate with dimensions 25mm×23mm×1.5mm is inserted within the ground and patch. The dimensions of radiating antenna which is placed on the substrate is 15mm×15mm×0.035mm for the Ultra-Wideband (UWB) antenna. The resonant frequency is 4.8GHz and 10.3GHz and its fractional bandwidth is 110% in the frequency range of the 3.1GHz to 10.7GHz. The simulation performed in HFSS 2021. All the parameters of the antenna are comparatively analyzed using the different types of the substrate materials like FR4, jeans, rubber, Rogers RT 5880 etc. and we have improved gain, SAR, efficiency parameters of an antenna. The proposed an antenna is applicable for on and off body centric communication. © 2023 IEEE.

关键词： Natural frequencies

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A Novel Long Term Evolution -Based Technique for Vehicle-to-Vehicle Communication as Design Implementation and Performance Evaluation

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SN computer Science 2025年第5期6卷 1-25页

作者： Chauhan, Mamta Astya, Rani Rakesh, Nitin Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University Greater Noida India Symbiosis Institute of Technology Nagpur Symbiosis International Deemed University Pune India

This work investigates the security problems associated with LTE networks that use automation. Mobile networks expansion creates substantial security risks because integrating automated systems for operational efficiency control. These security weaknesses through potential use cases while developing effective defense methods by using existing research findings and innovative security testing. Wireless networks control systems experience automated migration from manual to automated operations which delivers better performance efficiency and cost savings. The research focuses on analyzing security flaws in automated LTE networks especially those affecting V2V communication systems. LTE-based automation network development raises mobile network vulnerabilities that allow cyber threats to exploit protocol weakness through rogue base stations. The work evaluates these system weaknesses by demonstrating real attack models with Cisco Packet Tracer along with Kali Linux and Wire shark to detect network security loopholes. This work showcases how detrimental packet injections with unauthorized access and manipulated handover procedures affect LTE-V2X communication security. The proposed solutions from this research center on developing advanced authentication systems while implementing real-time monitoring frameworks together with regulatory improvements for securing vehicular communications. The study shows that effective V2V network protection needs multiple security measures to defeat upcoming cyber threats which will protect both efficiency and safety of the communication channels. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cisco packet tracer Cloud connecting Kali linux LTE V2V communication Wire shark Wireless technologies

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Approaches to Identify Paddy Plant Diseases Using Deep Learning: A Review 14

Approaches to Identify Paddy Plant Diseases Using Deep Learn...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Dixit, Atul Kumar Verma, Rajat Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350335095

To avoid a falling-off in the volume and production of agrarian products, it is crucial to determine Rice plant illnesses. The application of various ML and image-processing techniques lessens the issues in the agricultural industry. Since Rice feeds more than half of the world's population. Paddy is regarded as one of the most significant plants globally. Like other plants, rice is vulnerable to illnesses that could lower production levels and production of worse quality. Rice illnesses causes crop losses ranging from 20 to 40%. Farmers would need to be aware of the various diseases and be able to physically recognize diseases as early diagnosis of these diseases can favorably affect the yield. Rice Plant diseases may be self-diagnosed, which can assist cultivators to manage their crops more cleverly and enhance yields. Plant disease detection in crops using pictures is a challenging task by nature. This study examined the most pertinent five years paper on identifying various plant leaf diseases. Additionally, we outlined several issues with related plant leaf illnesses found by many machine learning and deep learning algorithm and their solutions. CNN are accurate at classifying and identifying images, many researchers have employed them to identify plant diseases. This Review Paper talked about the route that plant disease detection would go in the future as well as the advantages and drawbacks of employing various ML, DL, and Hybrid models for paddy plant disease detection. © 2023 IEEE.

关键词： Diseases

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OptFog: Optimized Mobility-Aware Task Offloading and Migration Model for Fog Networks 17

OptFog: Optimized Mobility-Aware Task Offloading and Migrati...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Alam, Mehbub Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Guwahati Department of Computer Science & Engineering India

ISBN: (纸本)9798350307672

Due to the increased usage of mobile devices, there are now diverse application needs that favor the use of Fog computing architecture over the Cloud's centralized architecture. Fog Computing arises in this circumstance, which moves certain processing resources to the edge or among themselves and provides low-latency access to networked devices. However, minimizing the number of migrations to utilize the limited resources at the fog devices is essential and challenging. In this regard, this paper presents OptFog (optimized mobility-aware predicted task migration model for fog-assisted networks) as a migration assistance model. Three machine learning models are employed to anticipate mobility and make proactive migration decisions. The data used in OptFog is from active users provided Luxembourg SUMO Traffic (LuST) from the city of Luxembourg. Machine learning models like Decision Tree, k-Nearest Neighbors (k-NN), and Random Forest are evaluated to estimate the user's future position. Thereafter, the best approach is determined by comparing them. The Random Forest model produces a significant result, with an overall accuracy of 97.47%. OptFog shows the overall time, downtime and energy consumption when employed with a prediction-based migration strategy and also analyzes the most elevated scenarios for task migration. Simulations have shown that migration can result in satisfactory effectiveness for supporting mobility in fog-assisted networks. © 2023 IEEE.

关键词： Fog computing

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A Robust Approach for Malaria Parasite Identification with CNN Based Feature Extraction and Classification using SVM 14

A Robust Approach for Malaria Parasite Identification with C...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ohdar, Kanak Nigam, Akriti Birla Institute of Technology Mesra Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350335095

Malaria is a fatal illness caused by the transmission of the Plasmodium parasite through Anopheles mosquitoes. It causes symptoms like fever, chill, headache, and fatigue. In severe cases, it also leads to organ failure and death. The conventional method of detecting malaria is through microscopy. The development of new tools and algorithms for malaria diagnosis and treatment is crucial. In this study, a CNN-based model has been proposed for the feature extraction from raw thin blood smear images. The extracted features are then classified into one of the four species types of namely Falciparum, Vivax, Ovale and Malariae using an SVM classifier. The proposed model achieves a significantly good accuracy of 98%, demonstrating the effectiveness of deep learning techniques in Malaria diagnosis and species classification. This study has significant implications for the development of new tools for Malaria diagnosis and treatment, which could help to speed up the process of detection and help the medical practitioner make an accurate analysis of infection, particularly in rural areas, where medical facilities are scarce. © 2023 IEEE.

关键词： Support vector machines

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A Robust Hybrid Cryptosystem based on DNA Steganography 8

A Robust Hybrid Cryptosystem based on DNA Steganography

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Chakraborty, Debadrita Bhowmik, Bishal Bhowmik, Susmita Debbarma, Pankaj Tripura Institute of Technology Computer Science and Engineering Department Tripura Agartala India

ISBN: (纸本)9798350396638

Cryptography is a major concern in the field of computer networks and security of data. By encoding a plain message using cryptographic algorithms, cryptography converts a plain message into an unintelligible format for humans. The advancement in cryptographic algorithm is a fundamental issue for data transmission in today's world. The field of deoxyribonucleic acid (DNA) based cryptography offers the possibility of resolving the issue efficiently. The work presents an innovative technique of data encoding using DNA sequence. In the proposed DNA steganography technique, the data is encoded to facilitate in hiding a secret message on a sequence of DNA strand using its bases, namely, adenine (A), guanine (G), cytosine (C) or thymine (T), very easily. This algorithm introduces two rounds of encryption within the DNA sequence. The plain text is converted to ASCII (American Standard Code for Information Interchange) code, which is a 7-bit character code with each individual bit representing a unique character. The proposed technique has been tested using random user input and found to give an index of coincidence (IC) value of 0.28, approximately. © 2023 IEEE.

关键词： Cryptography

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