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Computing and Data Analytics (ICCDA), International Conference on

作者： Christopher Ifeanyi Eke Fawaz Ibrahim Olamide Musa Phiri Mulenga Mwenge Faith Fatokun B Liyana Shuib Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa State Nigeria Department of Computer Science and Information Technology Mulungushi University Kabwe Zambia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia Department of Information Systems Faculty of Computer Science & IT University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

information security remains one of the major challenges faced by organizations and individuals in the current technological era. With the growing popularity of smart devices, the frequency of cyber-attacks targeted at these devices are increasing at a rapid pace. Among these types of attacks on information systems is malicious software also known as malware. A cyber-attack based on malware has the potential to cause loss or manipulation of confidential information. Currently, malware creators who use polymorphic techniques to evade detection can bypass traditional signature-based methods of detecting malware. Thus, various researchers have turned to Machine Learning (ML) techniques to enhance the detection of malware. However, despite the promising results, ML-based classification of malware remains a challenging task that the research community is yet to solve. Therefore, in this study, we propose an ML-based malware detection system that incorporates the AdaBoost, Random Forest, Decision Tree, and K-Nearest Neighbors Algorithm (KNN) algorithms into an ensemble stack with gradient boost as the ensemble boost for the stack meta estimator. We also use Extra Trees Classifier for feature selection. Multiple performance measures are utilized to assess the system's performance while using the Kaggle Malware Dataset. Our findings demonstrate that the proposed method successfully detects malware samples with remarkable accuracy, precision, recall, F1, and area under the curve (AUC) scores of 99.4%, 99.3%, 99.4%, 99.4%, and 99.6%, respectively.

关键词： Machine learning algorithms Nearest neighbor methods Feature extraction Malware Classification algorithms Decision trees Ensemble learning Random forests Cyberattack Tuning

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A Support Vector Machine Based Approach for Accuracy Enhancement in End Member Estimation from Hyper Spectral Images

A Support Vector Machine Based Approach for Accuracy Enhance...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Sunil Bhuvana, J. Gupta, Rakhi College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Management Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

This study affords a help vector system-based totally (SVMB) technique to beautify the accuracy of endmember estimation from hyperspectral snapshots. The research applies the SVMB method to the hyperspectral photograph band selection and endmember extraction using the guide vector regression (SVR) method to extract endmembers correctly from hyperspectral pix. The proposed model is examined with an artificial dataset composed of four substances and a real-international dataset of 4 forms of soil to illustrate the model's effectiveness as it should be estimating endmembers from the pix. The version is located to have significant improvement in phrases of accuracy compared with existing techniques. The proposed technique merges the characteristic extraction procedure, the selection of suitable bands, and the endmember extraction system into an unmarried level to decorate accuracy and reduce the time needed for hyperspectral photograph evaluation. The studies also propose a method to select appropriate bands with the assistance of SVR to sharpen the spectral data. The proposed version's outcomes show the proposed method's effectiveness for correctly estimating end members from hyperspectral snapshots. © 2024 IEEE.

关键词： Support vector machines

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Characterization of Vertical Wind Speed Profiles Based on Ward's Agglomerative Clustering Algorithm

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Journal of Modern Power systems and Clean Energy 2023年第5期11卷 1437-1449页

作者： M.C.Bueso A.Molina-García A.P.Ramallo-González A.Fernández-Guillamón Department of Applied Mathematics and Statistics Universidad Politécnica de Cartagena30202 CartagenaSpain Department of Automatics Electrical Engineering and Electronic TechnologyUniversidad Politécnica de Cartagena30202 CartagenaSpain Department of Information and Communications Engineering Computer Science FacultyUniversity of Murcia30100 MurciaSpain Universidad de Castilla La-Mancha Campus Universitario02071 AlbaceteSpain

Wind turbine blades have been constantly increasing since wind energy becomes a popular renewable energy source to generate electricity. Therefore, the wind sector requires a more efficient and representative characterization of vertical wind speed profiles to assess the potential for a wind power plant site. This paper proposes an alternative characterization of vertical wind speed profiles based on Ward's agglomerative clustering algorithm, including both wind speed module and direction data. This approach gives a more accurate incoming wind speed variation around the rotor swept area, and subsequently, provides a more realistic and complete wind speed vector characterization for vertical profiles. Real wind database collected for 2018 in the Forschungsplattformen in Nordund Ostsee(FINO) research platform is used to assess the methodology. A preliminary pre-processing stage is proposed to select the appropriated number of heights and remove missing or incomplete data. Finally, two locations and four heights are selected, and 561588 wind data are characterized. Results and discussion are also included in this paper. The methodology can be applied to other wind database and locations to characterize vertical wind speed profiles and identify the most likely wind data vector patterns.

关键词： Clustering algorithm wind speed data management power generation

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Enhancing Retrieval Quality Through Big Data and AI Technologies 3

Enhancing Retrieval Quality Through Big Data and AI Technolo...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Abhinav Singh, Akhilendra Pratap Shankar, Amar Kaushik, Harshita Divya, K. Disha, S. Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Food Technology Faculty of Engineering and Technology Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Wankhede Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

Huge facts and AI technologies have superior speedy to end up the primary drivers of innovation and progress in lots of one-of-a-kind areas, along with statistics retrieval. through the evaluation of big datasets of formerly retrieved documents and question logs, AI algorithms are capable of expect and perceive the maximum applicable files and also are used to higher recognize and respond to consumer queries. additionally, AI technology which include natural language processing are already beginning to shape the manner human beings engage with engines like Google, permitting extra conversational, person-friendly seek experience. eventually, massive information and AI have the capability to help form semantically based collections of documents with keywords and entities robotically extracted from content, making it easier for a search engine to analyze and connect files higher. This may in the end result in better retrieval satisfactory and greater dynamic records exploration studies for users of search engines. © 2023 IEEE.

关键词： analyze dynamic exploration records search

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Deep Learning-Based Technique for Sign Language Detection 2

Deep Learning-Based Technique for Sign Language Detection

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2nd International Conference on information technology Research and Innovation, ICITRI 2023

作者： Mustaffa, Zuriani Farihin Mohd Zulkifli, Nik Ahmad Sulaiman, Mohd Herwan Ernawan, Ferda Adam, Yagoub Abbker Universiti Malaysia Pahang Faculty of Computing Pekan Pahang26600 Malaysia Universitas Nusa Mandiri Depok Indonesia Jazan University Faculty of Computer Science and Information Technology Saudi Arabia

ISBN: (纸本)9798350324945

Sign languages are a form of communication used by the deaf and hard-of-hearing community. Malay Sign Language (MSL) is the official sign language practiced in Malaysia, enabling communication through hand signs and facial expressions. Each sign and its combination hold a distinct meaning, making it challenging for individuals to casually learn MLS. Therefore, this study presents an object detection model that utilizes the Single Shot Detector (SSD) and Mobilenet to detect MLS in real time. The model focuses solely on detecting static signs that do not involve complex combinations. The datasets used for training consist of 2000 sign images collected from Kaggle website, as well as images captured using a personal camera. The datasets were divided into training, validation, and testing phases in an 80:10:10 ratio, respectively. In conclusion, this study successfully developed a real-time and accurate system for recognizing MSL using the SSD-Mobilenet model. This contribution has significant implications for the field of sign language recognition and can greatly improve communication access for individuals who are deaf or hard-of-hearing. © 2023 IEEE.

关键词： Object detection

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Improving Machine Learning Techniques for Personalized Cardiac Arrest Risk Estimation

Improving Machine Learning Techniques for Personalized Cardi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Mahajan, Prerna Wadhwa, Durgesh Kumar, Amit Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Chemistry Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

This study researches the efficacy of gadget learning algorithms for predicting private cardiac arrest chances. Precise evaluation of existing methods and datasets for predicting cardiac arrest hazards could be performed to identify areas of improvement. Ultimately, unique system mastering algorithms and techniques may be tested and compared to modern-day fashions to identify the one that could offer the first-rate performance. Moreover, this research will provide insights into the finer info of version personalization to create custom fashions tailored for people consistent with their applicable chance profile. It will be carried out using information from numerous assets, including the affected person's clinical records, demographics, lifestyle, and environmental elements. Further, accuracy, sensitivity, and specificity parameters can be additionally evaluated to decide the most appropriate predictive fashions. The results of this research challenge will offer treasured insights into the utility of device studying techniques for customized chance estimation of cardiac arrest, which can be used to improve the accuracy and efficacy of outcomes. It technical summary introduces how to improve modern-day machine getting-to-know strategies for customized cardiac arrest threat estimation. The proposed method uses a convolutional neural community with a unique structure optimized for gaining knowledge of functions from medical information of cardiac arrest sufferers. In particular, the proposed structure utilizes a shared convolutional-recurrent layer for statistics extraction, accompanied by multiple fully connected layers, a dropout layer for noise regularization, and a softmax output layer with sigmoid-go entropy loss characteristic. The evolved model was examined on a cardiac arrest-affected person facts dataset and showed statistically considerable overall performance development compared to modern-day system learning procedures. These results illustrate the capabili

关键词： Hazards

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A Research on the Perspective of Exploring Restricted Decentralized Blockchain by Applying PoFE: Proof of Familiarity and Existence to Reinforce Multiple Domains 3

A Research on the Perspective of Exploring Restricted Decent...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sheela, K. Priya, C. Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Incorporation of Blockchain technology into our real-life scenarios serves in many aspects to improvise their services. Blockchain is a node-to-node decentralized distributed ledger system which provides clarity and unchangeable records of any digital assets while avoiding the intervention of a middleman. It is a new and revolutionary technology that is gaining a lot of attention because of its ability to eliminate threats and frauds on a large scale. The complete decentralized nature of blockchain has certain drawbacks wherein a restricted decentralized network can be accomplished in order to direct the overall process. The centralized server will be maintained along with its basic distributed nature with an authorized controller or miner. Every transaction will be stored in all the nodes and one node will be considered as a chief node wherein certain authenticating powers are provided to monitor and direct the operation in a successful manner. This process eliminates the involvement of multiple parties called miners to mine the block which reduces certain complications like time consumption for choosing miners, finding a fault node in case of breakdown and so on. Certain procedures are implemented to prove the presence of the records along with a process which encourages synergistic decision making for users using the Proof of Familiarity and Existence algorithm. Also, an approach of utilizing the nodes of any sector will be considered for smooth maintenance of an entire data in a blockchain platform. An approach of collaborating Proof of Familiarity and Proof of Existence will be carried out to enrich the decision-making system in both private and public sectors. © 2022 IEEE.

关键词： Blockchain

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An Explainable AI Techniques for Advancing Diabetes Prediction Using Machine Learning

An Explainable AI Techniques for Advancing Diabetes Predicti...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Saxena, Surabhi Parveen, Nikhat Yarlagadda, Bhanu Naga Karthik Srujan, Appasani Siva Siva Kumar, Mannava Ashok Likhitha, Koka Christ University Department of Computer Science Bangalore India College of Computing and Information Technology University of Bisha Department of Artificial Intelligence Saudi Arabia Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada Guntur India

ISBN: (纸本)9798331527495

Researchers have developed an automated system to identify diabetes risk. This system combines data from two sources: a collection of female patients in Bangladesh and an expanded dataset from a local textile factory. The expanded dataset includes information from 203 additional patients. The system uses several techniques to improve its accuracy. It first identifies the most important factors for predicting diabetes, then employs a special model to estimate insulin levels. It also addresses challenges like imbalanced data (where one outcome is more common) and explains its predictions using artificial intelligence techniques. This system achieved the superlative results has an 81.0% accuracy rate, 0.812 F1 score, and 0.844 Area Under the Curve (AUC).. These metrics indicate strong performance in identifying diabetes risk. © 2025 IEEE.

关键词： Textile mills

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Automated Door to Prevent COVID-19 using Fuzzy Logic 4

Automated Door to Prevent COVID-19 using Fuzzy Logic

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4th International Conference on Electrical, Communication and computer Engineering, ICECCE 2023

作者： Khokhar, Salah-Ud-Din Sohaib, Rana Muneeb, Abdul Bibi, Irfana Noor, Muhammad Yasir Imran, Muhammad School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Shandong Jinan China Department of Computer Science G.C. University Lahore Punjab Lahore Pakistan Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan Department of Electronics G.C. University Lahore Punjab Lahore Pakistan

ISBN: (纸本)9798350369694

Due to the vagueness and uncertainties due to the coronavirus, it is crucial to implement the standard operating procedures (SOPs) issued by the World Health Organization and the authorities. Therefore an effective control system is required that minimizes the vagueness and uncertainties of people who are on the front line during the period of this pandemic checking the entering persons in an indoor area where it is necessary to maintain a distance of six feet from one another to avoid the spread of coronavirus. In this research work, a robust and efficient system has been proposed that takes protective mask and feverishness values as input and applies them using the fuzzy logic algorithm. If these input values are according to the rules defined in the system, then the door will open as an output. Otherwise, the door will not open. It is, finally, analyzing the output values. This fuzzy model had been implemented using a microcontroller. The genuine difference in the duty cycle of the microcontroller’s output pulse was measured using a 100 MHz digital oscilloscope. Our proposed system was compared with different systems reported in the literature. This work reduces the human effort as well as the negligence of people and builds up the management system without the labor in a mint. © 2023 IEEE.

关键词： Fuzzy logic

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Secured Cloud Environment for Improved Web Services in Agricultural Sector 7

Secured Cloud Environment for Improved Web Services in Agric...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Kalaiselvi, V.K.G. Hariharan, Shanmugasundaram Kukreja, Vinay Venkateswarareddy, H. Hemanth, P. Dinesh, P. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of CSE Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, there has been an increasing demand for organic foods and home products due to their health benefits and eco-friendliness. However, the lack of standardization and certification has made it difficult for consumers to trust the authenticity of these products. This article presented here is a cloud-based application that enables consumers to purchase and verify the authenticity of organic foods and home products. The proposed system uses a cloud solution for ensuring product authenticity, while the cloud infrastructure provides scalability and security for the system. The application includes a user-friendly interface that allows consumers to easily browse and purchase products, while also providing detailed information about the product's origin and certification. The cloud-based solution ensures that consumer data is secured and protected from unauthorized access. Overall, the proposed system provides a secure and reliable platform for consumers to purchase and verify the authenticity of organic foods and home products. © 2023 IEEE.

关键词： Web services

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