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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Voditel, Preeti Gurjar, Aparna Kadoo, Pranjali Dahake, Tanaya Shri Ramdeababa Callege of Engineering and Management Department of Computer Application Nagpur India

ISBN: (纸本)9798350300857

In the agricultural sector, the scourge of plant diseases casts a long shadow, leading to substantial financial losses and threatening both crop quality and quantity. With global food demands on the rise and the expansion of crop cultivation to meet these needs, the urgency for effective, scalable, and prompt plant disease detection methods cannot be overstated. Traditional approaches reliant on manual visual inspections are fraught with labor intensiveness, time constraints, and inherent limitations, necessitating innovative solutions harnessing the capabilities of modern technology. The central goal of this system is to introduce a pragmatic, efficient, and adaptable approach that capitalizes on recent breakthroughs in neural network-based image analysis, with a specific focus on the transformative potential of Convolutional Neural Networks (CNNs). The proposed system encompasses several critical phases, commencing with image acquisition, followed by meticulous pre-processing, feature extraction, and culminating in classification via neural networks. What sets this system apart is its versatility, as it can be trained with a spectrum of algorithms, offering flexibility in adapting to various agricultural settings and unique plant disease scenarios. The outcomes resulting from the deployment of this multi-step plant disease detection system hold the promise of reshaping agriculture. It delivers timely and dependable insights into crop health, empowering farmers to make well-informed decisions and enabling the precision implementation of disease control strategies. By optimizing crop management practices through early disease identification, this system has the potential not only to mitigate the financial repercussions of diseases but also to bolster overall agricultural productivity. However, it is imperative to acknowledge that further research is indispensable. Such research should address potential limitations, fine-tune the system, and enhance its accuracy and

关键词： Extraction

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An Innovative Machine Learning Approach for Breast Cancer Detection Based on Comparative Analysis 2nd

An Innovative Machine Learning Approach for Breast Cancer De...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Das, Manab Kumar Bhattacharjee, Swapnamoy Mondal, Saikat Goswami, Soham Department of Computer Application and Science IEM-UEM Kolkata India Department of CSE MCKV Institute of Engineering West Bengal Howrah India

ISBN: (纸本)9789819776023

The basic building blocks of the human body are cells, and cancer is defined as an abnormal proliferation of bodily cells. There are however numerous other causes of cancer. Cancer has emerged as one of the world's most perilous causes of death in recent years. The fatality rate of Breast Cancer (BC) is increasing globally on a daily basis. The majority of women worldwide lost their lives as a result of BC. In 2020, an estimated 2,261,419 new breast cancer cases for women were reported worldwide. Histopathology image data analysis can be used to diagnose or forecast breast cancer in the majority of instances, but this type of analysis at the genetic level is very time-consuming and expensive. In this study, the main objective is to examine various well-known Machine Learning (ML) techniques, including Logistic Regression, Random Forest, KNN, and Decision Tree, in order to investigate BC. This proposed technique introduces a hybrid approach based on the overlap value of particular features and also attempts to calculate the accuracy at every stage. Additionally, thoroughly examine the correctness of the suggested system in comparison to existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Reliable and Stable Routing in VANET Using Multi-Objective Function Aided K-Means Clustering with Heuristic Algorithm 1

Reliable and Stable Routing in VANET Using Multi-Objective F...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Das, Nabanita Barooah, Maushumi Das, Anupam Assam Engineering College Jalukbari Department of Computer Application Assam Guwahati781013 India The Assam Royal Global University Betkuchi Department of Computer Science and Engineering Guwahati781035 India

ISBN: (纸本)9798350307795

The expansion of Vehicular Ad-hoc Networks (VANET) has brought a heightened focus on service delivery within the realm of the smart transit systems. Nevertheless, the mobility attributes inherent to vehicular networks often result in frequent route disruptions, particularly when transmitting data. This circumstance holds true in both advanced and emerging nations, where substantial time is wasted due to congested traffic conditions. These consequences are substantial, encompassing heightened stress levels for drivers, reduced efficiency for diverse personal and commercial vehicles, and escalated emissions of harmful gases, especially those that contribute to air pollution, affecting the well-being of residents in densely populated regions. Clustering stands as a formidable approach for establishing a stable topological arrangement. Therefore, an efficient reliable routing scheme is developed in VANET with the help of a clustering algorithm. The node clustering is initially performed with the help of the K-means Clustering algorithm (KMC) in VANET. An Advanced Pelican Optimization Algorithm (APOA) is utilized for choosing a shorter and more stable route between all available routes to make efficient routing in VANET. The multi-objective functions like delay, hop count, energy, and the ratio of packet delivery are considered for the efficient routing in VANET to make energy-efficient transmission. The effectiveness of the proposed KMC-based optimal routing in VANET is validated with the conventional routing schemes in accordance with some of the metrics to determine efficacy. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Intelligent Farming: IoT-Powered System for Efficient Soil Condition Analysis 13

Intelligent Farming: IoT-Powered System for Efficient Soil C...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Sharma, Nandini Singh, Shiv Shanker Khenwar, Medha Gupta, Priyanka Gla University Department of Computer Engineering and Application Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Smart farming technology allows farmers to control and analyse variables in real-time, helping them optimize crop growth and manage their operations more efficiently. The use of specialized sensors, data collection tools, and advanced analytics in Internet of Things (IoT) systems is helping to bridge the gap between physical farm conditions and digital monitoring. In this study, we aim to develop a system that monitors soil conditions to support smart farming. The system provides farmers with live data on key environmental factors, such as temperature, humidity, soil moisture, and soil temperature, to improve crop yield and product quality. The IoT setup for this intelligent farming solution includes components like the DHT11 and humidity sensors, a DS18B20 temperature probe, LDR, water pump, and a 12V LED strip, all controlled by a NodeMCU. When the system is activated, it continuously monitors soil and environmental conditions and sends this information to the cloud for real-time access. If the soil moisture falls below a set threshold, the system automatically triggers irrigation. Our experimental results show that this model improves decision-making for managing farming conditions. © 2024 IEEE.

关键词： Smart agriculture

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An Integrated Security VANET Algorithm for Threat Mitigation and Performance Improvement Using Machine Learning

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SN computer Science 2024年第8期5卷 1089页

作者： Khanna, Heena Kumar, Mukesh Bhardwaj, Vivek School of Computer Application Lovely Professional University Punjab Phagwara 144411 India Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India School of Computer Science and Engineering Manipal University Jaipur Jaipur 303007 India

Vehicular Ad-Hoc Networks (VANETs) have emerged as a captivating field of research due to the escalating number of vehicles on the road in recent years. Ensuring a secure Intelligent Transportation System (ITS) is imperative for passenger and driver safety. However, the dynamic nature of VANETs presents challenges for real-time implementation. This study proposes an enhanced security algorithm tailored for VANETs, adept at mitigating threats such as Denial of Service Attacks (DoS), Sybil, and Replay attacks. The proposed method employs an Enhanced K-Means approach to form clusters for diverse attacks, coupled with a hybrid technique utilizing Support Vector Machine (SVM) and Feed-forward backpropagation for classifier accuracy assessment which incorporates firefly algorithm for optimization. The findings demonstrate notable enhancements in terms of Throughput, Jitter, True Detection Rate, and Packet Delivery Ratio (PDR). Lastly, we delineate future directions and identify open issues for further investigation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： DoS Machine learning Security Support vector machine VANET

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Detection of Cyber Attack in IoT Based Model Using ANN Model with Genetic Algorithm 5

Detection of Cyber Attack in IoT Based Model Using ANN Model...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Srivastava, Animesh Sharma, Hemendra Shanker Rawat, Rishikesh Garg, Navin Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University Department of Computer Science & Application Roorkee India Bansal College of Engineering Bhopal India Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

Thanks to the IoT, our everyday lives are now more accessible, connected, and convenient than ever before. Because it facilitates the effortless transfer of massive amounts of data between interconnected devices, it leaves the network vulnerable to various forms of intrusion and attack. As the number of interconnected devices grows, the need for reliable attack detection systems to forestall malicious actions becomes paramount. This paper introduces a strategy for detecting attacks on the Internet of Things (IoT) that integrates Genetic Algorithms (GA) and Artificial Neural Networks (ANN) models. This paper introduces a model for an artificial neural network that uses genetic algorithms to detect attacks on the Internet of Things. While the GA adjusts the hyperparameters of the ANN model, the ANN model is responsible for determining whether network traffic is malicious or not. To evaluate the proposed system, an Internet of Things network traffic dataset was utilized. As demonstrated by the outcomes, the system has a commendable rate of detection. The system can spot DoS attacks. The results show that our method is great at finding attacks on the Internet of Things. we achieved a 99.3 percent accuracy rate with our suggested ANN with GA. © 2024 IEEE.

关键词： artificial neural networks attack detection deep learning Internet of Things

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The Role of Machine Learning in Optimizing Industrial Communication Protocols

The Role of Machine Learning in Optimizing Industrial Commun...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Kharbas, Vikas Kumar Mahesh, T.R. Upadhyay, Satish Vivekananda Global University Department of Computer Science & Application Jaipur India Faculty of Engineering and Technology Department of Computer Science Engineering Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Due to the complexity and urgency of information exchange in modern companies, optimizing communication protocols in the workplace is essential. Using methods from machine learning, this research takes a fresh tack toward optimizing the performance of communication protocols. The suggested approach integrates Reinforcement Learning (RL), Convolutional Neural Networks (CNNs), and Genetic Algorithms (GAs) to provide a self-optimizing system with dynamic behavior. The RL feature facilitates this protocol's flexibility in the face of dynamic network circumstances by providing rapid feedback. CNNs are used for real-time anomaly detection to improve safety and dependability. GAs methodically explores combinations of protocol parameters to determine the optimal collection that matches specified performance criteria. The recommended technique was compared to the usual methodology using a variety of efficiency and reliability measures. The findings demonstrate that the proposed approach frequently outperforms the state-of-the-art methods in a broad range of commercial settings with regards to efficiency, dependability, and adaptability. Understanding how to enhance industrial communication protocols is crucial in the age of Industry 4.0 and the IIoT. The recommended solution offers an autonomous, adaptable, and secure tool for boosting communication in dynamic production situations. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach 2

Deep Learning Techniques for Identification of Pneumonia: A ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Ruchika Nayak, Debasish Swapnesh Kumar Rout, Chinmayee Priyadarshini Jena, Lambodar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India Nalanda Institute of Technology Department of Computer Application Bhubaneswar India Siksha 'O Anusandhan University Department of Data Science Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9798350370188

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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