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L-Arginine Supplementation Mitigates Dichlorvos-Induced Haematocardiotoxicity, and Oxidative Stress in Male Wistar Rats

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Open Journal of applied Sciences 2024年第7期14卷 1886-1903页

作者： Waidi Adeoye Saka Yusuff Dimeji Igbayilola Jabba Hamidu Lawan Taoheed Kolawole Muftaudeen Ridwanullahi Adejumo Tolulope Deborah Alu Mary Ikuomola Joshua Tinuade Ojelere Victor Odufa Adegoke Department of Physiology College of Health Sciences Ladoke Akintola University of Technology Ogbomoso Oyo State Nigeria Department of Human Physiology College of Medicine and Health Sciences Baze University Abuja Nigeria Department of Biological Sciences Faculty of Computing and Applied Sciences Baze University Abuja Nigeria

Due to its toxicity, dichlorvos—a common organophosphate pesticide—poses significant risks to human health. This study utilized male Wistar rats to explore the potential protective effects of L-arginine supplementation against dichlorvos-induced toxicity, focusing on cardiotoxicity, haematotoxicity and oxidative stress. The rats were divided into four groups: Control, L-arginine (L), Dichlorvos (D), and L-arginine + Dichlorvos (L + D). Dichlorvos was administered to the D group, L-arginine (100 mg/kg) to the L group, and both L-arginine and dichlorvos to the L + D group. The study evaluated various parameters, including cardiovascular, oxidative stress markers, and haematological indices. Significant changes in haematological parameters such as haemoglobin (Hb), haematocrit (HCT), and red blood cell count (RBC) indicated haematotoxicity after dichlorvos administration. Additionally, elevated cardiac markers, including lactate dehydrogenase (LDH) and creatine kinase-MB (CK-MB), suggested cardiotoxic effects. Exposure to dichlorvos also resulted in decreased antioxidant enzyme levels and increased oxidative stress indicators like malondialdehyde (MDA). Remarkably, L-arginine supplementation mitigated the damage caused by dichlorvos. It normalized the altered haematological parameters, demonstrating its protective effect against haematotoxicity. The rise in cardiac markers was reduced with L-arginine supplementation, indicating protection against cardiotoxicity. Moreover, L-arginine significantly decreased oxidative stress, as evidenced by lower MDA levels and restored antioxidant enzyme activity. In conclusion, L-arginine supplementation in male Wistar rats showed promising protective effects against dichlorvos-induced cardiotoxicity, haematotoxicity and oxidative stress. This suggests that L-arginine may offer a beneficial intervention to mitigate the adverse effects of dichlorvos on blood and heart health, paving the way for potential treatments for pesticide poison

关键词： Dichlorvos L-Arginine Cardiovascular Function Haematological Parameters Oxidative Stress

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Improved Attentive Recurrent Network for applied Linguistics-Based Offensive Speech Detection

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Computer Systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN Computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Classification of Retroviruses Based on Genomic Data Using RVGC

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Computers, Materials & Continua 2021年第12期69卷 3829-3844页

作者： Khalid Mahmood Aamir Muhammad Bilal Muhammad Ramzan Muhammad Attique Khan Yunyoung Nam Seifedine Kadry Department of CS&IT University of SargodhaSargodha40100Pakistan Department of CS&IT University of MianwaliMianwali42200Pakistan School of Systems and Technology University of Management and TechnologyLahore54782Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

Retroviruses are a large group of infectious agents with similar virion structures and replication ***,cancer,neurologic disorders,and other clinical conditions can all be fatal due to retrovirus *** of retroviruses by genome sequence is a biological problem that benefits from computational *** National Center for Biotechnology Information(NCBI)promotes science and health by making biomedical and genomic data available to the *** research aims to classify the different types of rotavirus genome sequences available at the ***,nucleotide pattern occurrences are counted in the given genome sequences at the preprocessing *** on some significant results,the number of features used for classification is reduced to *** classification shall be carried out in two *** first phase of classification shall select only two *** data in the first phase is transferred to the next phase,where the final decision is taken with the remaining three *** data sets of animals and human retroviruses are selected;the training data set is used to minimize the classifier’s number and training;the validation data set is used to validate the *** performance of the classifier is analyzed using the test data ***,we use decision tree,naive Bayes,knearest neighbors,and vector support machines to compare *** results show that the proposed approach performs better than the existing methods for the retrovirus’s imbalanced genome-sequence dataset.

关键词： Retroviruses machine learning bioinformatics classification

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Meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

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Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Utilizing a machine learning algorithm to choose a significant traffic identification system

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International Journal of Information Management Data Insights 2024年第1期4卷

作者： Natarajan, Rajesh Ranjith, Christodoss Prasanna Mohideen, M．Syed Khaja Gururaj, H.L. Flammini, Francesco Thangarasu, N. Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Al-Aqr Shinas 324 Oman Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences- Salalah Post Box No 608 Salalah 211 Oman Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India IDSIA USI-SUPSI University of Applied Sciences and Arts of Southern Switzerland Switzerland Department of Computer Science Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

The tremendous advancements in computing, sensing, and cognitive-based revolution have made way for critical infrastructure needed to improve the internet for more extensive applications. However, the computation performed by devices and the ability to communicate is limited due to the use of numerous devices for storage. Among the most complex model scenarios, a framework based on a Traffic Identification System (TIS) is a crucial answer. The Web-based brute force and botnet attacks are linked in this initial phase. To forecast the samples using the adaptive bootstrap method, the second phase entails creating a classifier on the training dataset. The normalization model and features are combined in the model, which then identifies traffic attacks on the network. Based on voting mechanisms from several decision trees utilized in the mode, an ensemble model using the Adaptive Boosting Classifier and Random Forest Algorithm (ABC-RFA) method is employed to construct the classification model. According to the overhead analysis of the dataset used for testing, the suggested model's overall performance is approximately 98.90 %, much better than the conventional models currently in use. Overall Precision, Specificity, Rate of Detection, And Accuracy Were All Attained. © 2024 The Author(s)

关键词： Adaptive bootstrap Classification Ensemble Model Random Forest (RF)

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive computing in Engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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An Integrated Deep Learning Framework for Fruits Diseases Classification

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Computers, Materials & Continua 2022年第4期71卷 1387-1402页

作者： Abdul Majid Muhammad Attique Khan Majed Alhaisoni Muhammad Asfand Eyar Usman Tariq Nazar Hussain Yunyoung Nam Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Science and Engineering University of Ha’ilHa’ilSaudi Arabia Department of Computer Science Bahria UniversityIslamabad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway

:Agriculture has been an important research area in the field of image processing for the last five *** affect the quality and quantity of fruits,thereby disrupting the economy of a *** computerized techniques have been introduced for detecting and recognizing fruit ***,some issues remain to be addressed,such as irrelevant features and the dimensionality of feature vectors,which increase the computational time of the ***,we propose an integrated deep learning framework for classifying fruit *** consider seven types of fruits,i.e.,apple,cherry,blueberry,grapes,peach,citrus,and *** proposed method comprises several important ***,data increase is applied,and then two different types of features are *** the first feature type,texture and color features,i.e.,classical features,are *** the second type,deep learning characteristics are extracted using a pretrained *** pretrained model is reused through transfer ***,both types of features are merged using the maximum mean value of the serial ***,the resulting fused vector is optimized using a harmonic threshold-based genetic ***,the selected features are classified using multiple *** evaluation is performed on the PlantVillage dataset,and an accuracy of 99%is achieved.A comparison with recent techniques indicate the superiority of the proposed method.

关键词： Fruit diseases data augmentation deep learning classical features features fusion features selection

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks 7

Enhancing IoT-Edge Computation with Data Forwarding based De...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Prasad, B.M.G. Jopate, Rachappa Savita, Pankaj Basi Reddy, A. Shankar, B. Prabu Arunkumar, M.S. Department of Cse Andhra Pradesh Kurnool518452 India University of Technology And Applied Sciences - Al Mussanah Department of Information Technology Al Muladha Muscat Oman Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Department of Cse Madhya Pradesh Bhopal India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India Alliance University Alliance College of Engineering And Design Department of Computer Science And Engineering Karnataka Banglore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371406

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices' processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model's performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems. © 2023 IEEE.

关键词： Edge computing

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An Effective Approach for Detection of Racism and Abusive Memes Using CNN, Roberta and BERT Algorithm

An Effective Approach for Detection of Racism and Abusive Me...

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computing and Data Analytics (ICCDA), International Conference on

作者： V Joseph Raymond Geogen George Bhuvan Beera Amit Pimpalkar School of Computing SRM Institute of Science and Technology Kattankulathur India University of Technology and Applied Science Ibri Oman SRM Institute of Science and Technology Kattankulathur India Department of Computer Science (AIML) Sri Ramdeobaba College of Engineering and Management Nagpur

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

Memetics, as a social media phenomenon, are a self-evident part of the rise and rise of social media through electrifying technologies. They are intricate to classify using traditional forms of technologies because they are mainly based on both visual and textual information and more often than not they are also funny or sarcastic. The work puts forward a deep learning-based solution for meme categorization that can be classified into multiple categories. The include girl, tame, politics, hard. It emphasizes the want for an automated system that can vet memes before they become the cause of controversy or become humorous. This investigation offers the three-step procedure to locate racism and abusive memes. Before the text is labeled as racist or not, it will be extracted from the supplied image first. Once the language has been pinpointed as unacceptable, the third step is to distribute the information to the specific groups: racist and abusive are: unoffensive, nonrelevant. We present a deep learning-based method for classifying memes that can be categorized into different themes. After the graphical theft of personal information for the marketing of products through social media meme posting, society had to confront the issue of these instance being posted on the social media platform. Racist and abusive memes can cause emotional harm to people and even divide communities since they are mainly genocidal or ridiculing. The constraint of the language complexity and the variety of artistic references used in memes leave some of them generally feting and removing racist and vituperative memes. We introduce a deep literacy- attained asseveration to classify wicked memes using CNNs, Roberta, as well as BERT. When we had the inflamed dataset of and thenon-racist and vituperative memes, we machined the models. Our experimental study was carried out through the adoption of several metrics like learning accuracy, precision, recall, and F1-score. Racist and abusive memes ar

关键词： Memetics Measurement Learning systems Visualization Data analysis Accuracy Social networking (online) Nails Virtual environments Complexity theory

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