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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSai 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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Integration of MCC to detect disease in plants with DL Health Industry 4

Integration of MCC to detect disease in plants with DL Healt...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bewerwal, Anubhav Habelalmateen, Mohammed I. Alsailawi, H.A. Venkatesan, D. Dhabli, Dharmesh Jabbar, Qasim Abdul Bader, Nabaa M. Alwan, Ali Saad Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq St.Martin's Engineering College Dept.of Ai&ml Secundrabad India Vishwakarma Institute of Information Technology Pune India Alkunooze University College Medical Instrument Department Basra Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

Mobile gadgets are progressively assuming a vital role in individuals' everyday routines as they streamline several practical activities. Mobile cloud computing overcomes the constraints of memory, CPU, and battery life, and enhances its functionalities by integrating mobile and cloud computing. Big data analytics solutions facilitate the extraction of value from data by considering four key factors: volume, diversity, velocity, and validity. The study explores the use of mobile cloud computing and big data analytics in the context of networked healthcare. The focus is on the justification and development of interconnected healthcare applications and systems, highlighting the rise of cloud computing in this domain. The paper presents a cloudlet-based mobile cloud computing architecture designed specifically for big data applications in the healthcare sector. In the proposed framework the study assess two types of models that is cloud-based model and Wi-fi Cloudlet-based model, the working mechanism model describe the differentiation in processing. Furthermore, this study evaluates the results obtained and compares both mechanism based on latency in processing. © 2024 IEEE.

关键词： Electronic health record

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Extending LLMs to New Languages: A Case Study of Llama and Persian Adaptation 31

Extending LLMs to New Languages: A Case Study of Llama and P...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Sani, Samin Mahdizadeh Sadeghi, Pouya Vu, Thuy-Trang Yaghoobzadeh, Yadollah Haffari, Gholamreza Department of Electrical and Computer Engineering University of Tehran Iran Tehran Institute for Advanced Studies Khatam University Iran Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761964

Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) using parameter-efficient fine-tuning. We employ a multi-stage approach involving pretraining on monolingual Persian data, aligning representations through bilingual pretraining and instruction datasets, and instruction-tuning with task-specific datasets. We evaluate the model's performance at each stage on generation and classification tasks. Our findings suggest that incorporating the Persian language, through bilingual data alignment, can enhance classification accuracy for Persian tasks, with no adverse impact and sometimes even improvements on English tasks. Additionally, the results highlight the model's initial strength as a critical factor when working with limited training data, with cross-lingual alignment offering minimal benefits for the low-resource language. Knowledge transfer from English to Persian has a marginal effect, primarily benefiting simple classification tasks. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Image Forgery Detection Using MD5 & Open CV 2

Image Forgery Detection Using MD5 & Open CV

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Shaikh, Mohammad Shahnawaz Biswal, Aparajita Pandwal, Akruti Khodifad, Nilesh Vaghela, Bhavesh Parul University Parul Institute of Engineering and Technology Department of AI and AIDS Vadodara India Parul University Computer Science & Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798331534950

The more the advanced image making tools become accessible, the more image forgery is proliferated in hardware and software across all these domains - forensics, journalism, authentication, etc. In this research paper, we investigate methods on how to detect forged images by using Python programing, MD5 hashing algorithm and OpenCV library. The first part of the study utilizes MD5 to do an integrity verification by generating a unique hash value for each original image. The digital fingerprint you get from hashing each step of the image is this hash, and is a powerful way to compare against suspected forged images. MD5 is not perfect, but it is useful for performing an initial check of manipulated site;while not perfect, it does the trick. As powerful image processing software, OpenCV is used to enhance the detection process. This library provides detailed analysis of pixel patterns, possibly strange color discrepancies or just other things that would hint a manipulation. The proposed method performs advanced anomaly detection upon the extracted features from both original and suspect images with the aim of discovering inconsistencies meant to imply forgery. The research then proves through a series of experimental validations that the use of MD5 coupled with OpenCV significantly increases detection accuracy, while reducing false positives and detecting both overt and subtle manipulation. This not only underlines the need for creating a multilayered approach to counter the challenges posed by image forgery but also introduces a hybrid framework called UEFRG that combines three different methods done specifically to address image forgery. As digital content evolves, this study indicates the necessity of reliable detection methods to ensure integrity of visual media. The algorithm is further refined and machine learning techniques are explored for incorporation for future work to further improve the detection capabilities to keep pace with emerging threats in digital

关键词： Image matching

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Recognizing Human Emotions by using Lazy classifier with selected algorithm 9

Recognizing Human Emotions by using Lazy classifier with sel...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Loganathan, V. Mohanaprakash, T.A. Bhavani, R. Savija, J. Saveetha Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India St Joseph College of Engineering Department of It Chennai India Velammal Engineering College Department of Ai & Ds Chennai India

ISBN: (纸本)9798350384208

Music predates language, and humans are born with the ability to communicate themselves via music. Music has played a vital part in human history, and it is incorporated into many facets of human life Music is showing tremendously significant role in people's production and life. This research is focusing a n identification of human emotions like happiness, sad, relaxation, tension on Turkish music emotion dataset through inductive learning approach. This work is to focus on the various lazy learning approaches are implemented to recognize human emotions while listening music. The K=3 with Ball Tree NN search model acquires highest performance which as accuracy 85.75%, kappa 0.66, precision 0.86, F-Measure0.86, recall 0.86, Matthews Correlation Coefficient 0.65 which generated the best outcomes based on their efficacy in comparison to other models. The K=3 with Ball Tree model produces best result for recognizing human emotions compare with other models. © 2023 IEEE.

关键词： Trees (mathematics)

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NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs

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computers, Materials & Continua 2023年第10期77卷 1303-1323页

作者： Sikandar Khan Khalid Saeed Muhammad Faran Majeed Salman A.AlQahtani Khursheed Aurangzeb Muhammad Shahid Anwar Department of Computer Science Shaheed Benazir Bhutto University SheringalDir(U)Khyber Pakhtunkhwa18200Pakistan Department of Computer Science Kohsar University MurreePunjab47150Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-si13120South Korea

Delay Tolerant Networks(DTNs)have the major problem of message delay in the network due to a lack of endto-end connectivity between the nodes,especially when the nodes are *** nodes in DTNs have limited buffer storage for storing delayed *** instantaneous sharing of data creates a low buffer/shortage ***,buffer congestion would occur and there would be no more space available in the buffer for the upcoming *** address this problem a buffer management policy is proposed named“A Novel and Proficient Buffer Management Technique(NPBMT)for the Internet of Vehicle-Based DTNs”.NPBMT combines appropriate-size messages with the lowest Time-to-Live(TTL)and then drops a combination of the appropriate messages to accommodate the newly arrived *** evaluate the performance of the proposed technique comparison is done with Drop Oldest(DOL),Size Aware Drop(SAD),and Drop Larges(DLA).The proposed technique is implemented in the Opportunistic Network Environment(ONE)*** shortest path mapbased movement model has been used as the movement path model for the nodes with the epidemic routing *** the simulation results,a significant change has been observed in the delivery probability as the proposed policy delivered 380 messages,DOL delivered 186 messages,SAD delivered 190 messages,and DLA delivered only 95 messages.A significant decrease has been observed in the overhead ratio,as the SAD overhead ratio is 324.37,DLA overhead ratio is 266.74,and DOL and NPBMT overhead ratios are 141.89 and 52.85,respectively,which reveals a significant reduction of overhead ratio in NPBMT as compared to existing *** network latency average of DOL is 7785.5,DLA is 5898.42,and SAD is 5789.43 whereas the NPBMT latency average is *** reveals that the proposed policy keeps the messages for a short time in the network,which reduces the overhead ratio.

关键词： Delay tolerant networks buffer management message drop policy ONE simulator NPBMT

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A Novel Approach to Avoid Road Traffic Accidents and Develop Safety Rules for Traffic Using Crash Prediction Model Technique 6th

A Novel Approach to Avoid Road Traffic Accidents and Develop...

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6th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2022

作者： Ahammad, Sk Hasane Sukesh, M. Narender, Mekala Ettyem, Sajjad Ali Al-Majdi, Kadhum Saikumar, K. Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Guntur India Department of Computer Science and Engineering Vaagdevi College of Engineering Telangana Warangal India Department of Computer Science and Engineering Jayamukhi Institute of Technological Sciences Narsampet Warangal India National University of Science and Technology Muscat Iraq Ashur University College Baghdad Iraq School of Engineering Department of AI-ML Malla Reddy University Telangana Hyderabad500043 India

ISBN: (纸本)9789811995118

The expansion of nations and communities has resulted in a variety of externalities, such as an increase in traffic accidents. Many attempts have been undertaken to minimize the injuries and fatalities and their intensity. Traffic safety modeling is a most significant technique to motivate harmless mobility because it is capable of the creation of Crash Prediction Models (CPMs) as well as the investigation of the fundamentals that contribute to the incidence of crashes. Statistical modeling has been utilized in this process in the past, regardless of the fact that they are aware of the limits of this sort of strategy which allows you to experiment with other options, such as using machine learning approaches. Machine learning approaches applied to collision datasets can assist researchers in better knowing the features of motorist behavior, highway surroundings, and meteorological circumstances that are linked to varying mortality risk levels. If we build a reliable predictive model capable of automatically classifying the degree of injury in diverse traffic accidents, we may be able to discover patterns involved in severe wrecks. These patterns of behavior and road accidents can be used to design traffic safety rules. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Advancing Plant Disease Identification using Convolutional Neural Networks for Precise Diagnosis in Hibiscus Plants

Advancing Plant Disease Identification using Convolutional N...

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Artificial Intelligence and Emerging Technology (Global ai Summit), International Conference on

作者： Nandita Goyal Shivani Agarwal Tripti Choudhary Usikela Naresh Laith H. Alzubaidi Ram Sharma Department of Technology Technology Ajay Kumar Garg Engineering College Ghaziabad India Department of CSE-AIML Hi-Tech Engineering College Ghaziabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India The Islamic University Najaf Iraq Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The effectiveness and accuracy of deep learning models for the precise diagnosis of diseases in Hibiscus plants are studied. Gray mold, mainly caused by Botrytis cinerea, in diseased Hibiscus leaves is identified using 560 images collected to form the dataset. Preprocessing of the obtained dataset is performed through resizing, data augmentation, and noise reduction, which ensures the variability of the dataset. The preprocessed dataset is employed to train four different deep learning architectures: custom CNN, ResNet-50, VGG-16, and VGG-19. The benefits of using each model are that they are able to extract features and learn the pattern of the distribution of these features in the presence of the disease. The results are obtained based on the accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve for comparison purposes. According to the results, the best candidate is VGG-19, which demonstrates accuracy at the level of 98.54%, while the precision of 98.80%, recall of 98.50%, F1 score of 98.65%, and AUC-ROC of 99.00% are also high. The confusion matrices provide details from which the correct classification for VGG-19 of healthy and diseased leaves becomes evident. The real-time implementation of VGG-19 is completely reliable. The applied deep learning models provide numerous benefits in predicting the diseases in plants, and the usage of computational techniques will ensure food security and opportunities for current development.

关键词： Deep learning Training Plant diseases Accuracy Computational modeling Receivers Feature extraction Convolutional neural networks Reliability Artificial intelligence

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A Novel CAD Framework with Visual and Textual Interpretability: Multimodal Insights for Predicting Respiratory Diseases 8

A Novel CAD Framework with Visual and Textual Interpretabili...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Mukhlis, Raza Saleem, Saied Kwon, Hyunwook Hussain, Jamil Aydin, Ahmet Arif Al-Antari, Mugahed A. Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of Inonu University Faculty of Engineering Department of Computer Engineering Malatya Turkey

ISBN: (纸本)9798331531492

Generating textual interpretability using recent advancements in large language models (LLMs) is crucial for enhancing the efficiency of comprehensive computer-aided diagnosis (CAD) systems. This improves transparency between medical staff, intelligent CAD systems, and end-users by creating a trustworthy and effective intermediate medical diagnosis environment. In this paper, an innovative explainable throughout CAD system is introduced, designed to predict diseases from Chest X-rays (CXR) in a comprehensive scenario. The primary goal is to undertake multiple tasks that reduce the burden on medical staff and enrich CAD outcomes, including classification, visual explanations (heatmaps), and textual report generation. The proposed CAD system is developed through eight key steps: Data Collection and Annotation, Data Preparation, Text Vectorizations (Indexing), Visual Encoder, RAG-Fusion, Structural Prompt, Xai LLmTextual Reasoning (LLM Model), and Final Output (LLM textual report, image classification, and heatmap localization). The ai-based CAD system is trained and evaluated using the public benchmark MIMIC-CXR dataset with 14 different classes. The classification performance achieved an overall accuracy of 70 %, precision of 70 %, and F1-score of 0.60 %, while for text report generation, the system obtained an average BERTScore precision of 0.83, RougeL 0.16, and a Meteor score of 0.28. These promising results suggest the potential for further improvement of the CAD system and its applicability to real-world medical tasks. © 2024 IEEE.

关键词： Diagnosis

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Threat Eye: Behavior Analytics for Cloud Security using ml

Threat Eye: Behavior Analytics for Cloud Security using ML

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny S. Shivaspandana K. Pavan N. Muthukumaran K. Akash Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Cloud computing has developed for its storage and accessibility of data, providing flexibility, scalability, and affordability. But its extensive use has also resulted in cyberattacks and unauthorized access. The traditional intrusion detection mechanisms become ineffective for different attack patterns, insider attacks and zero-day exploits. This paper provides an efficient way to address this issue by providing a mechanism to identify the threat based on the behaviour of the user. Unlike traditional approaches this paper provides a system that monitors the user behaviour patterns considering the features such as login history, authentication attempts, session duration, file access activities, password change frequency and IP location modifications. By considering these features the attack can be detected, and the data access is restricted to prevent from unauthorized access. This paper provides handling of attack with a combination of machine learning algorithms including SVM, Decision tree and XBoost, these help in distinguishing between the attacker and the authorized user. This helps in identifying complex attack behaviours and detect insider threats. Additionally, RSA encryption is integrated to secure data transmissions, preventing unauthorized access. By combining behavioural analysis, attacks prediction, and encryption, this provides a robust cybersecurity framework.

关键词： Support vector machines Logistic regression Machine learning algorithms Cloud computing security Scalability Intrusion detection Passwords Threat assessment Encryption Monitoring

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