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41st International Conference on Machine Learning, ICML 2024

作者： Shubham, Kumar Jayagopal, Aishwarya Danish, Syed Mohammed Prathosh, A.P. Rajan, Vaibhav Department of Electrical Communication Engineering Indian Institute of Science Karnataka Bengaluru India Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Indian Institute of Technology Bihar Patna India

Cancer, a leading cause of death globally, occurs due to genomic changes and manifests heterogeneously across patients. To advance research on personalized treatment strategies, the effectiveness of various drugs on cells derived from cancers (‘cell lines') is experimentally determined in laboratory settings. Nevertheless, variations in the distribution of genomic data and drug responses between cell lines and humans arise due to biological and environmental differences. Moreover, while genomic profiles of many cancer patients are readily available, the scarcity of corresponding drug response data limits the ability to train machine learning models that can predict drug response in patients effectively. Recent cancer drug response prediction methods have largely followed the paradigm of unsupervised domain-invariant representation learning followed by a downstream drug response classification step. Introducing supervision in both stages is challenging due to heterogeneous patient response to drugs and limited drug response data. This paper addresses these challenges through a novel representation learning method in the first phase and weak supervision in the second. Experimental results on real patient data demonstrate the efficacy of our method WISER (Weak supervISion and supErvised Representation learning) over state-of-the-art alternatives on predicting personalized drug response. Our implementation is available at https://***/kyrs/WISER. Copyright 2024 by the author(s)

关键词： Self-supervised learning

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Internet of things (IoT)-based on real-time and remote boiler fuel monitoring system: a case of Raha Beverages Company Limited, Arusha-Tanzania

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Discover Internet of Things 2023年第1期3卷 19页

作者： Ntambara, Boniface Nyambo, Devotha G. Solsbach, Andreas Department of Embedded and Mobile Systems School of Computational and Communication Science and Engineering The Nelson Mandela African Institution of Science and Technology Arusha Tanzania United Republic of Department of Computing Science University of Oldenburg Ammerländer Heerstr114-118 Oldenburg26129 Germany

RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilizes fuel in steam production in Arusha-Tanzania, where fuel data conditions such as temperature, pressure, discharge, fuel level, and gas leakage with humidity were a challenge to monitor, which provoked boiler malfunction and plant breakdown. Today, RABEC manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time-consuming and gives inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure sensors were used to gather fuel information where the GSM module was employed to send fuel data messages to the operator’s phone. An AT mega 328 microcontroller was used to process and analyse the fuel data and send them to the Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the threshold value, an operator was alerted by a refilling message via GSM technology. A pressure of 0.1psi, a fuel temperature of 120 ℃, and 80% of humidity, the system notifies the operator by an alert message to check the injector pressure and if the fuel–air mixture is perfect. In addition, these data were observed on the LCD and ThingSpeak webpage. To conclude, the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and efficiency rate compared to the current monitoring system. © The Author(s) 2023.

关键词： Microcontrollers

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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Computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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GenreNet: A Deep Based Approach for Music Genre Classification

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SN Computer Science 2024年第8期5卷 1135页

作者： Bala Ganesh, N. Bhuvaneswari, M.S. Bhagavathi Sankar, K. Ganesh, P. Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Department of Smart Computing Vellore Institute of Technology Tamilnadu Vellore India Ciena India Haryana Gurgaon India Tata Consultancy Services Tamilnadu Chennai India

Neural network-based Music Genre Classification is a key component in helping users narrow down the selection of songs and listen to music in a certain genre. Audio segmentation has been done as a preprocessing step on the available audio files using the benchmark dataset “GTZAN”. To improve accuracy, audio segmentation divides a 30-s audio recording into ten 3-s audio files. Segments are utilized to create spectrograms, which are then used to extract features. A spectrogram is a visual depiction of a signal’s frequency band. Applying techniques like magphase and Short Time Fourier Transform to the audio data enhances the quality of the spectrograms. Adaptive Moment estimation, Nesterov-accelerated Adaptive Moment estimation (NADAM), Adaptive Moment estimation with Maximum, and Root Mean Squared Propagation are among the optimization techniques used for feature weight optimization to increase the classification rate. NADAM is found to be effective based on performance measures. The proposed GenreNet model is compared with the state-of-art techniques and performance measures such as Accuracy, Precision, Recall, Root Mean Square Error, F1 Score, Loss and Sensitivity have yielded positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alexnet18 Audio classification CNN MGC Music genre classification Spectrogram SVM

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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Computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy 9

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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9th International Conference on Advanced computing and Communication systems, ICACCS 2023

作者： Chinnasamy, A. Selvakumari, P. SRMIST School of Computing Department of Data Science and Business Systems Chennai India Anna University Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd's clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found. © 2023 IEEE.

关键词： Mean square error

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SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION OF RAW MATERIALS CONSIDERING DEMAND FLUCTUATIONS 51

SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION OF RAW M...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Sarker, Mohiuddin Sakib, Nazmush Ahmed, Sayem Chakrabortty, Ripon K. Department of Mechanical & Production Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh Department of Industrial Engineering Southern Illinois University EdwardsvilleIL United States School of Systems & Computing University of New South Wales CanberraACT2610 Australia

Industries that face a dynamic market always struggle to find the optimal supplier and ordering policy. The rapid changes in demand also pose significant challenges for decision makers. Thus, to assist the decision-makers with supplier selection and optimal ordering policy under demand uncertainties, a multi-criterion, multi-period, and multi-objective mixed integer linear programming (MILP) model is proposed by considering procurement costs, lead times, inventory, shortage and carbon emission costs. Sales data of a pharmaceutical company is collected, and the future demand is forecasted using the Prophet forecasting model. The output of forecasted demand is then used as the input of the MILP model. Weighted objective, preemptive goal programming and non-preemptive goal programming are used to address the challenge of multi-criteria mathematical programming. The decision makers will be able to choose the best supplier among many suppliers and will also be able to get the ordering policy and inventory policy for each time period. A sensitivity analysis is done to study the impact of inventory factors and shortage factors on the objective values. The proposed framework can be used as a decision-making tool by corporate organizations to assist supply chain managers in assessing supplier selection and order allocation by predicting demand while keeping the sustainability of the environment in mind. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Sensitivity analysis

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Evaluation of Factors that Impede the Use of Construction 4.0 Technologies for Construction Safety Management

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Journal of engineering, Project, and Production Management 2025年第1期15卷 1-14页

作者： Desai, Vimlesh Prabhu D’souza, Lysette Singh, Anil Bhadauria, Vikram Management Studies Department Agnel Institute of Technology and Design Goa Assagao403507 India V M Salgaocar Institute of International Hospitality Education Manora – Raia Goa Salcete 403720 India Department of Information Systems and Operation Management College of Business University of Texas ArlingtonTX76019 United States Department of Information Systems and Computing Technologies College of Business Engineering and Technology Texas A&M University TexarkanaTX75503 United States

The safety performance of the construction sector remains subpar despite the utilization of both conventional and new methodologies. Consequently, there is a decline in the standard of work, the efficiency of employees, and an escalation in project expenses and duration. In order to enhance safety, it is imperative for the industry to use cutting-edge digital technologies at every stage of the project’s lifespan. The utilization of these technologies is not yet prevalent and, by far, may be a factor in the poor safety record in the construction industry. This study aims to identify and evaluate factors that impede the use of such technology to improve health and safety in construction management. Literature review and interviews with a few experts were able to garner 18 factors, which were evaluated by experienced industry professionals on a 5-point Likert scale using a questionnaire survey. The responses were analyzed using the relative importance index (RII) and factor analysis. The analyses indicated ‘Employees’ resistance and reluctance to change’, ‘high upfront investment’, ‘lack of awareness about digital technologies and their benefits’, ‘limited trained workforce to work on digital technologies’, and ‘poor data communication infrastructure facilities’ as the top five barriers. Factor analyses churned out six groups: ‘Organization readiness’, ‘Industry readiness’, ‘Country readiness’, ‘Technology related’, ‘Data related’, and ‘Investment related’ on the basis of latent characteristics. The findings will aid firms, the government, and academia in directing resources and planning strategies to improve the usage of C4.0 technologies in safety and health management in the construction industry. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Construction industry

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Organizational Elements that Shape Construction Employees’ Approach and Behavior Towards OSH

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Journal of engineering, Project, and Production Management 2025年第1期15卷 1-11页

作者： Desai, Vimlesh Prabhu D’souza, Lysette Singh, Anil Bhadauria, Vikram Management Studies Department Agnel Institute of Technology and Design Goa Assagao403507 India V M Salgaocar Institute of International Hospitality Education Manora – Raia Salcete Goa 403720 India Department of Information systems and Operation Management College of Business University of Texas ArlingtonTX76019 United States Department of Information systems and computing technologies College of Business Engineering & Technology Texas A&M University TexarkanaTX75503 United States

The construction industry has one of the highest known rates of occupational incidents. Construction organizations are increasingly recognizing the significance of occupational safety and health (OSH) problems as a prominent area of concern. Though numerous studies have shown that employees’ behavior and approach towards OSH are impacted by Environmental (such as governmental regulations), Personal and Organizational factors, there is a lack of consensus on the organizational elements. Hence, the primary objective of this study is to ascertain the organizational elements that influence employees’ adherence to OSH and suggest improvement needs, if any. To achieve the objective, the study adopted a qualitative research approach and conducted semi-structured interviews with fifteen highly experienced professionals in the construction industry. Using manual thematic analysis, the interview transcripts were analyzed to identify and extract major themes based on commonalities. Six themes were recognized: Empowerment, communication, knowledge management and learning and development, leadership and top management commitment, internal systems and processes, and work culture and spirituality. The findings of this study will assist the industry in developing a framework of effective solutions for OSH management. These themes can form the basis of a good approach to an integrated OSH strategy. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Construction industry

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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