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Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops

computer Systems science & engineering 2023年第9期46卷 3847-3864页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana murthy Ravuri Daniel E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenangMalaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science University of Central AsiaNarynKyrgyzstan Faculty of Computers and Information South Valley UniversityQenaEgypt

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged,and due to the pest attacks,the quality is *** are the major reason behind crop quality degradation and diminished crop ***,accurate pest detection is essential to guarantee safety and crop *** identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological ***,some progress has been made in agriculture by employing machine learning(ML)to classify and detect *** study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops(MMTL-IPCAC)*** presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization(CLAHE)approach for image *** neural architectural search network(NASNet)model is applied for feature extraction,and a modified grey wolf optimization(MGWO)algorithm is employed for the hyperparameter tuning process,showing the novelty of the *** last,the extreme gradient boosting(XGBoost)model is utilized to carry out the insect classification *** simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%.

关键词： Sustainable agriculture crop monitoring pest management insect classification computer vision

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A Twin Dual-Arm Robot Testbed to Collect the Human Demonstration Data for Imitation Learning 13

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstra...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kim, Juhyung Heo, Sungwon Kim, Songwoo Jung, Ho-Jin Yoon, Han Ul Yonsei University Division of Computer and Telecomm. Engineering Mirae Campus Wonju Korea Republic of Graduate School Yonsei University Department of Computer Science Wonju Korea Republic of Yonsei University Division of Software Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798350373332

Collecting consistent human demonstrations has been a problematic issue in the field of a learning from demonstration. This paper presents a twin dual-arm robot testbed to collect human demonstration data while performing manual tasks in the activities of daily livings such as opening/closing a window, opening-up/closing-down a sauce bottle cap, etc. The testbed consists of the two sets of a dual-arm robot;for each robot, one-side arm has 6-DoF with a crab-gripper as its hand. Two dual-arm robots are mounted at the front and back of a table-type station. A human operator performs actions to complete a given task with the one at the back;the human operator's actions, meanwhile, are duplicated by the other robot at the front simultaneously. The experimental results show the testbed's potential to gather consistent human demonstration data for training a machine learning agent to manipulate the dual-arm robot under an imitation learning framework. © 2024 IEEE.

关键词： Bottles

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A Nudity Detection Algorithm for Web-based Online Networking Platform 10

A Nudity Detection Algorithm for Web-based Online Networking...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Ravulakollu, Kiran Kumar Goyal, Mayank Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Technology Woxsen University Hyderabad India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Social media and online platforms have become an integral part of the day-to-day life of the people and the organizations, which shows a lot of diversity. Many online platforms allow users to share media extensively, like images, videos, and audio but not all the media content shared on the internet is safe for work or should be restricted from particular platforms. Images with nudity are of big concern when it comes to online media sharing and social platforms must take serious measures to restrict users to share images with nudity. The rate of growth of nudity content over the internet or social media is very high. It affects the mental and physical health of individuals. In this paper, a novel method for nudity detection in images is presented. We aim to develop a Machine Learning algorithm for nudity detection in a community networking platform and how it should be integrated with such a platform so that posted images can be thoroughly checked for nudity and if detected how it should be dealt with. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Social networking (online)

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A Novel Approach Using Machine Learning for Accurate Detection of DDoS Attack in software Defined Network 1

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

作者： Sharma, Hitesh Kumar Choudhury, Tanupriya Kotecha, Ketan Mahdi, Hussain Falih Dutta, Soumi Bhattacharya, Abhishek Sar, Ayan Energy Acres Bidholi Uttarakhand Bidholi India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Pune India Pune India Department of Computer and Software Engineering University of Diyala Baquba Iraq Sister Nivedita University Chakpachuria India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

A denial Of Service (DoS) attack is termed an attack when the request-receiving server or network shuts down or becomes inaccessible for service. This kind of attack is caused by flooding the target server or network with huge traffic or sending information which can trigger a vulnerability to the server and lead to server crash. Different kinds of DoS attacks can occur on a network including a classic DoS attack which is carried out by a single client machine sending tons of requests and data to a server by itself to increase its processing to the extent that no new genuine requests are answered by the server and network is ultimately unreachable. Another kind of DoS attack is termed a DDoS (Distributed Denial Of Service) attack which includes a master host machine that leverages the network of machines around it to send multiple requests to the server from different machines which act as zombie machines for the attack as the machine won’t even have an idea itself that they are being used for an attack on the server. As multiple requests and data hits the server from different locations, the server becomes unavailable for processing and leads to network failure. These zombie machines are termed botnets which are connected online and are used to overwhelm a target website with fake traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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Use of digital platforms for enhancing academic performance. Systematic Review 7

Use of digital platforms for enhancing academic performance....

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Costescu, Cristina Anamaria Şogor, Mǎlina Pǎlimariu, Sabina Katona, Jozsef Tufar, Ioana Avram, Carla Maria Babe-Bolyai University Special Education Department Cluj-Napoca Romania Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills. © 2024 IEEE.

关键词： academic performance children digital devices

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Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ChatGPT in software Development Strategies

Beyond Agile: NLP-Driven Quality Attributes Retrieval Using ...

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engineering & Computing Technologies (ICECT), International Conference on

作者： Aamana Qurat Ul Ain Sardar Un Nisa Department of Software Engineering NUML Rawalpindi Pakistan Department of Computer Science NUML Islamabad Pakistan

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

This study explores Agile software Development (ASD) methodologies' effectiveness in achieving quality software products. It investigates the underexplored application of ChatGPT as a Quality Attribute Extractor in ASD. Utilizing hard prompt techniques, ChatGPT extracts quality attributes from user stories without fine-tuning, enhancing understanding through few-shot and zero-shot Prompt engineering. This innovative approach bridges NLP advancements with agile methodologies, facilitating efficient quality assessment in software development processes.

关键词： Natural languages Decision making Agile software development software quality Chatbots software systems Quality assessment

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Significance of State-of-Art Search Engine in Game Development 10

Significance of State-of-Art Search Engine in Game Developme...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Jagadam, Naveen Goyal, Mayank Kumar Ravulakollu, Kiran Kumar Sharan, Bhagwati Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Ceo N2force Technologies Inc. NapervilleIL60563 United States School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India School of Technology Woxsen University Hyderabad India School of Engineering and Sciences Srm University Department of Computer Science and Engineering AP Amaravati India

ISBN: (纸本)9789380544472

Video game development process is different from other software projects while yet being related to them in software engineering. It is distinctive in that it incorporates the work of teams from several fields (art, music, acting, programming, etc.), and that it seeks to create a compelling gaming experience by using prototypes and iterations. This research study focuses on game development and how it is accomplished utilizing a game engine. The process of creating/developing games from a concept is known as game development. Game engines are software programs that help game developers create games. The literature study discusses about the various game engine. The utility of the 'Unity' gaming engine is investigated in this paper. Unity may be utilized in different fields as study subjects, but there can be certain frequent benefits & obstacles of utilizing it in different case studies. Students learn which game engine to use based on their specific requirements. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Search engines

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ML-Driven HR System: Candidate Assessment for Enhanced Recruitment Outcomes 3

ML-Driven HR System: Candidate Assessment for Enhanced Recru...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Dixon, G.C. Srithanujan, S. Ranaweera, H.D.T.N. Ariyarathne, R.M.P.M. Weerasinghe, K.L. Gamage, N. Department of Information Technology SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer System engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka Department of Computer Science and Software Engineering SriLanka Institure of Information Technology New Kandy Rd Malabe Sri Lanka

ISBN: (纸本)9798331532420

This study developed and implemented a machine learning-driven HR system designed to enhance recruitment outcomes, particularly in online interviews. Challenges such as candidate impersonation, interview question memorization, and deepfake manipulations were addressed. The system incorporated several critical processes utilizing advanced algorithms: pre-interview shortlisting employed Support Vector Machines (SVM) achieving 92% accuracy;cognitive skills assessment utilized adaptive testing methods powered by fine-tuned Large Language Models (LLMs) with 88% accuracy;emotional intelligence evaluation was conducted using deep learning-based emotion detection algorithms attaining 85% accuracy;face verification leveraged Convolutional Neural Networks (CNNs) for identity and deepfake detection with 90% accuracy;online video interviews were analyzed through integrated audio-visual processing techniques;language proficiency testing employed Natural Language Processing (NLP) models reaching 87% accuracy;analysis of thinking patterns was performed using sentiment analysis algorithms with 83% accuracy;and candidate grading was facilitated by ensemble learning methods ensuring consistent and reliable evaluations. The results demonstrated the system’s effectiveness in verifying candidate authenticity and assessing competence, providing a robust solution for modern recruitment challenges. © 2024 IEEE.

关键词： Support vector machines

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm 6

Web Application to predict Skin Disease: An Approach Using D...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Multi-Objective Binary Coordinate Search for Feature Selection

Multi-Objective Binary Coordinate Search for Feature Selecti...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Lab Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350337020

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II. © 2023 IEEE.

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