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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Verma, Charu Vaibhav Mahrach, Younes singh, Shweta Kumar, Ashok Rai, Vertika Singh, Gauri Computer Science and Engineering Prestige Institute of Engineering Management and Research Indore Madhya Pradesh Indore India Higher Institute of Nursing Professions and Technical Health of Tangier Tetuan Morocco Electronics and Communication Department IES College of Technology Bhopal India Kanpur India Allied Health Sciences Brainware University Kolkata India Chitkara Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

Prompt detection of malaria is crucial in order to avert outbreaks. Deep learning has exhibited exceptional performance in instances where it comes to identifying tissue images and analyzing cell morphology. Although extensive research has been conducted on the application of deep learning to malaria diagnosis, the majority of these studies have concentrated on binary classification tasks involving structures such as red blood cells and nuclei. This work examines malaria recognition in multiple stages and to introduce the Neighbor Sample Joint Learning (NSJL) model. Neighborhood relationship mining, Convolutional Neural Network (CNN) feature learning, and graph feature integration are all components of NSJL. The process involves the extraction of CNN features, which are subsequently fed into a Graph Convolutional Network (GCN) via a K-nearest neighbor’s adjacency graph. In order to assess NSJL, it is juxtaposed with cutting-edge methodologies. The findings suggest that the NSJL model attains the following performance metrics: 92.50% accuracy, 92.84% precision, 92.50% recall, and 92.52% F1 score. These outcomes illustrate the capability of the method to detect malaria, as its accuracy is at least 7% greater than that of competing approaches. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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

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

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

作者： Gaurav Soni Kamlesh Chandravanshi Devashri Deoskar Sadhana Parashar Ruchi Thakur School of Computing Science and Engineering VIT Bhopal University Kothrikalan Sehore Bhopal-Indore Highway Madhya Pradesh India Dept. of Computer Science and Engineering Lakshmi Narain College of Technology Madhya Pradesh India Dept. of Computer Science and Engineering Bansal Institute of Research and Technology Bhopal Madhya Pradesh India Dept. of Computer Science and Engineering SATI Vidisha Madhya Pradesh India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

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.

关键词： Measurement Electric breakdown Wireless networks Government Bandwidth Delay estimation Routing Load management Routing protocols Vectors

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Blockchain & IoT Based Smart Home Health Monitoring System with a Natural User Interface 3

Blockchain & IoT Based Smart Home Health Monitoring System w...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Munirathinam, T. Ameta, Gaurav Kumar Lavanya, P. Sudheer, S. Nagrath, Surekha Chandramauli, Awadhesh Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Sathyamangalam India Indus University Indus Institute of Technology and Engineering Department of Computer Engineering Ahmedabad India Bhavan's Vivekananda College of Science Humanities and Commerce Department of Physics and Electronics Telangana Hyderabad India Vignan's Foundation for Science Technology and Research Department of management studies AP Guntur India Medicaps University Rau Department of Management and Commerce Indore India Uttaranchal University Department of Civil Engineering U.K. Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Recently, the Industry 4.0 transformation meets up with a motion-based secure communication framework through smart-home IoT healthcare gadgets in assisting aged persons or else persons, who have some specific requirements and similar advancement in the technology has been growing. Such a system makes use of a decentralized blockchain agreement to store smart-home IoT healthcare information along with customer identification. For storing primitive mixed media IoT tactile payload and motion information, the structure employs an off-chain solution. A smart mortgage holder or expert organization might create a digital real place with a solid computerized hard drive for every person as well as recognized Internet-of-Things healthcare gadgets using our proposed health checking structure. Many allowed house occupants can collaborate with detectors applied to monitor IoT-enabled smart-home supervising detectors, enroll clients also Internet-of-Things wellbeing tangible medium, and transmit value-based parameters with the use of security symbol, in addition to unrefined Internet-of-Things wellbeing information updating via signal. Here, the modeling, as well as execution information of the smart-home wellbeing observation system, is proposed, including motion-based communication utilizing wellbeing gadgets like AI-locks and lights, those use decentralized BC in safety, faith, computerised personality, also security protection. © 2022 IEEE.

关键词： Blockchain

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Experimental Investigation for Hybrid Energy Harvesting System for Remote Monitoring Applications

Experimental Investigation for Hybrid Energy Harvesting Syst...

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Computing, sciences and Communications (ICCSC), International Conference on

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

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

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.

关键词： Wireless sensor networks Data visualization Real-time systems Hybrid power systems Maintenance Energy harvesting Kinetic energy Sustainable development Remote monitoring Wireless fidelity

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New Decision-Making Technique Based on Hurwicz Criteria for Fuzzy Ranking

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computers, Materials & Continua 2022年第12期73卷 4595-4609页

作者： Deepak Sukheja Javaid Ahmad Shah G.Madhu K.Sandeep Kautish Fahad A.Alghamdi Ibrahim.S.Yahia El-Sayed M.El-Kenawy Ali Wagdy Mohamed Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia Department of Mathematics Dr.APJ Abdul Kalam University Indore(M.P)India Department of Information Technology VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadIndia LBEF Campus Kathmandu44600Nepal Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Laboratory of Nano-Smart Materials for Science and Technology(LNSMST) Department of PhysicsFaculty of ScienceKing Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Research Center for Advanced Materials Science(RCAMS) King Khalid UniversityP.O.Box 9004Abha 61413Saudi Arabia Nanoscience Laboratory for Environmental and Biomedical Applications(NLEBA) Semiconductor Lab.Department of PhysicsFaculty of EducationAin Shams UniversityRoxyCairo 11757Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Department of Mathematics and Actuarial Science School of Sciences Engineering The American University in CairoEgypt

Efficient decision-making remains an open challenge in the research community,and many researchers are working to improve accuracy through the use of various computational *** this case,the fuzzification and defuzzification processes can be very *** is an effective process to get a single number from the output of a fuzzy *** defuzzification as a center point of this research paper,to analyze and understand the effect of different types of vehicles according to their *** this paper,the multi-criteria decision-making(MCDM)process under uncertainty and defuzzification is discussed by using the center of the area(COA)or ***,to find the best solution,Hurwicz criteria are used on the defuzzified ***-making technique is proposed using Hurwicz criteria for triangular and trapezoidal fuzzy *** proposed technique considers all types of decision makers’perspectives such as optimistic,neutral,and pessimistic which is crucial in solving decisionmaking problems.A simple case study is used to demonstrate and discuss the Centroid Method and Hurwicz Criteria for measuring risk attitudes among *** significance of the proposed defuzzification method is demonstrated by comparing it to previous defuzzification procedures with its application.

关键词： Defuzzification decision-making fuzzy numbers Hurwicz multicriteria decision-making ranking order

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Near-Real-Time Detection of Craters: A YOLO v5 Based Approach

Near-Real-Time Detection of Craters: A YOLO v5 Based Approac...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： Sourish Chatterjee Shayak Chakraborty Anirban Nath Pinaki Roy Chowdhury Benidhar Deshmukh Discipline of Geology School of Sciences Indira Gandhi National Open University New Delhi India Department of Computer Science and Engineering National Institute of Technology Silchar Assam India Department of Computer Science and Engineering Indian Institute of Technology Indore Madhya Pradesh India Defence Geoinformatics Research Establishment Chandigarh India

In planetary surfaces like that of Mars, impact craters are the most commonly found geological structures that a spacecraft may encounter while landing or navigation. Automated extraction of these craters in real-time can improve terrain relative navigation of a spacecraft. It can improve precision of a spacecraft’s position estimate by providing supplementary measurements to correct for drift in the inertial navigation system. It is also very important to precisely locate craters, especially smaller ones during the event of a soft landing. This calls for an automated system that can detect craters with high accuracy in near real time. To tackle this problem we propose to use a YOLO based detection because YOLO tackles detection as a single regression problem, straight from image pixels to bounding box coordinates and class probabilities. It is a single Convolutional Neural Network (CNN) simultaneously predicting bounding boxes and respective class probabilities for each bounding box. The YOLO network is trained on a crater dataset created from Mars Reconnaissance Obiter’s CTX and tested on both CTX and Mars Express mission’s HRSC images. The fully optimized CNN showed promising detection results, achieving state-of-the-art detection precision, with excellent recall at an astounding speed of 64.8 ms for 115 craters.

关键词： Space vehicles Mars Interplanetary exploration Inertial navigation Reconnaissance Position measurement Real-time systems

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Natural Language Processing for Generating Synthetic News Detection

Natural Language Processing for Generating Synthetic News De...

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International Conference on Contemporary Computing and Informatics (IC3I)

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

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

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.

关键词： Training Translation Accuracy Text summarization Production Natural language processing Reliability Fake news Informatics

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An Improved Machine Learning and Deep Learning based Breast Cancer Detection using Thermographic Images

An Improved Machine Learning and Deep Learning based Breast ...

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Electronics and Renewable Systems (ICEARS), International Conference on

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

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.

关键词： Deep learning Training Renewable energy sources Ultrasonic imaging Computational modeling Neural networks Predictive models

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

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

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International Conference on Computing and Networking technology (ICCNT)

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

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

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.

关键词： Training Support vector machines Analytical models Technological innovation Accuracy Computational modeling Time series analysis Anomaly detection Tuning Testing

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Artificial Intelligence Assisted IoT Data Intrusion Detection 4

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Shukla, Amogh Ahamad, Shahanawaj Rao, G.Nageswara Al-Asadi, Avein Jabar Gupta, Ankur Kumbhkar, Makhan Research Assistant Vellore Institute of Technology Vellore India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Dept. of EEE Mylavaram India Computer Institute of Sulaimani Dept. of Computer Program Iraq Vaish College of Engineering Dept. of Computer Science and Engineering Rohtak India Christian Eminent College Dept. of Computer Science Indore India

ISBN: (纸本)9781665414470

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world. © 2021 IEEE.

关键词： Internet of things

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