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Optimal aeration management strategy for a small-scale food waste composting

Carbon Resources Conversion 2024年第1期7卷 79-88页

作者： Jia Chi Lai Yi Lung Then Siaw San Hwang Chung Sien Lee School of Engineering and Science Faculty of EngineeringComputing and ScienceSwinburne University of Technology93350 KuchingSarawakMalaysia Department of Electrical and Electronic Engineering Faculty of EngineeringUniversiti Malaysia Sarawak94300 Kota SamarahanSarawakMalaysia

Food waste is a significant contributor to greenhouse gas emissions when it ends up in *** turns out to be a sustainable solution to this problem,but it requires controlled and continuous airflow for optimal *** study focused on the effect of aeration rates and airflow directions on food waste composting using a closed system with forced *** was entered into the composting vessel in three directions,which were upward,downward,and a combination of both *** direction was run at aeration rates of 0.1,0.4,and 0.7 L/*** findings showed that the compost pile aerated at 0.4 L/min by using two-directional airflow can reach the thermophilic temperature within half of the *** compost pile achieved temperature of 40.94℃ after 10.5 *** the compost experienced slightly high in moisture loss(4.3%),the compost still attained the standard values for *** compost produced from food waste could be applied in soil to improve its fertility.

关键词： Small-scale composting Two-directional airflow Intermittent aeration Food waste

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Smart Nutrient Deficiency Prediction System for Groundnut Leaf

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Intelligent Automation & Soft computing 2023年第5期36卷 1845-1862页

作者： Janani Malaisamy JebakumarRethnaraj Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengalpattuTamil Nadu603203India

Prediction of the nutrient deﬁciency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative *** fertiliser in optimum amounts will protect the environment’s condition and human health *** identiﬁcation also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deﬁciency through image *** chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deﬁciency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be *** model’sﬁrst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areﬂat-tened and given to the dense layer(DL)***,the Maximum Margin clas-siﬁer(MMC)is deployed and takes the input from DL for the classiﬁcation process toﬁnd *** dataset used in this work has no visible symptoms of a deﬁciency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deﬁciency before perceivable *** performance of the implemented model is analysed and compared with ImageNet pre-trained *** result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

关键词： CNN chlorophyll groundnut crop deep learning nitrogen deﬁciency

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A Sentimental Analysis Approach for Personalized Drug Recommendations Using Machine Learning 1

A Sentimental Analysis Approach for Personalized Drug Recomm...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Vijayaraj, A. Murugan, V.P. Jebakumar, R. Gaurav, N.V. Santhosh, V. Shai Kumar, R. R.M.K. Engineering College Department of Information Technology Chennai India Panimalar Engineering College Department of Mathematics Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350365337

Since the virus that causes corona disease was discovered, it has gotten more difficult to find medical professionals with the appropriate licenses, including doctors, nurses, diagnostic equipment, and medications. Due to the intense grieving, a substantial number of people in the medical industry pass away. Because of the scarcity, a lot of individuals began self-medicating without first consulting a doctor, which made the health problem worse. There are numerous well-established applications for machine learning, and recent years have witnessed a growth in the pace and breadth of computerization-related research and development. The goal of this research is to develop a method for recommending medications that might greatly reduce the obligations of specialists. In this work, we built a drug recommendation system that makes estimations about the emotional of patient reviews using a variety of different vectorization techniques, such as using a Bow, The TF-ID Word 2 Vector, or Manually Feature Analysis. The predicted emotions were graded using accuracy, remembrance, flscore, exactness, and the area beneath the slope (AUC). The results show that, when weighed against the other models, the classification technique Linear SVC + The TF -I vector process has the greatest accuracy. © 2024 IEEE.

关键词： Recommender systems

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The prevalence of Internet and smartphone addiction: A growing digital dependency in the modern age 29

The prevalence of Internet and smartphone addiction: A growi...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing popularity and accessibility of the Internet and smartphones have prompted studies examining the potential adverse effects of excessive use. The researcher is investigating problematic smartphone use and dependence (PSUD) and its implications for individuals' wellbeing. PSUD, also known as Internet addiction or smartphone addiction, refers to the compulsive use of smartphones and the Internet, negatively impacting an individual's life. Considering smartphones' modern and advanced nature, they serve as versatile devices encompassing various internet uses, making them the most common technological devices used to access the internet. Therefore, when researchers address technology or internet use-related issues, they often focus on smartphones and examine PSUD, which can also cover other technology-related problems. This interchangeability of terminologies allows researchers to study a broad spectrum of technology use issues through smartphone analysis. Research has shown that PSUD can lead to various negative consequences, including psychological, behavioural, and social problems. These issues encompass anxiety, depression, poor sleep quality, academic and occupational challenges, relationship difficulties, and impaired social and emotional functioning. The factors contributing to PSUD's development are complicated and involve personal, environmental, technological, and socio-demographic elements. Individual factors may include mental health problems, poor coping skills, low self-esteem, and a lack of social support. Environmental factors may involve a lack of parental supervision, peer pressure, and cultural norms encouraging excessive internet and smartphone use. Technological aspects may encompass app and website designs that promote addictive behaviours. Socio-demographic factors, such as age, gender, and socioeconomic status, can also influence smartphone use patterns and contribute to PSUD. The effects of PSUD are both psychological and behavioura

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SecureML: Machine Learning-Powered Malware Detection 2

SecureML: Machine Learning-Powered Malware Detection

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Awasthi, Animay Ahsan, Mohd Vanusha, D. Delsi Robinsha, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu District Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350383867

As malware threats become increasingly complex, the development of advanced detection systems is necessary to protect digital assets. SecureML Using Machine Learning presents an innovative approach to combat the escalating threat of malicious software by leveraging machine learning algorithms. This research paper outlines the design, implementation, and evaluation of SecureML, emphasizing its effectiveness in detecting both known and novel malware strains. Through comprehensive feature selection techniques and algorithmic comparison, SecureML identifies crucial features and selects the Random Forest algorithm as the most effective for malware detection, achieving an impressive accuracy rate of 99.40% on the test set. The system's low false positive and false negative rates validate its robustness and reliability in accurately distinguishing between benign and malicious files. SecureML demonstrates significant potential in bolstering cybersecurity defenses and safeguarding digital assets against evolving malware threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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Computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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The emergence of technological dependencies: A critical review of problematic smartphone use and dependence (PSUD) 29

The emergence of technological dependencies: A critical revi...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

Problematic smartphone use has emerged as a growing concern in modern society. However, research in this field is still in its early stages and faces several controversies and limitations. A significant challenge lies in the need for a standardised definition of smartphone dependence and addiction, making it difficult to compare results across studies and generalise findings to the larger population. To address this issue, I advocate using the umbrella term Problematic Smartphone Use and Dependence (PSUD) to encompass all conditions related to excessive and problematic use, addiction, and smartphone dependence. An ongoing debate centres on whether addiction and dependence models are appropriate for understanding PSUD, leading researchers to explore different approaches to this complex phenomenon. Some studies have relied on self-reported methods, while others have used objective criteria, such as tracking the time spent on specific apps. The causal relationship between smartphone use and adverse outcomes, such as anxiety, depression, and sleep disturbances, is still inadequately understood. Some recent investigations have observed strong correlations, while others have found weak or inconsistent associations. Not all high smartphone use is problematic. Practical reasons, such as the requirements of particular roles or daily responsibilities, may drive high levels of smartphone use. High use levels appear to increase as more smartphone apps become available, and adoption is now widespread. Differentiating high use (for legitimate reasons) from problematic use (e.g. uncontrolled or unnecessary use) requires new terminologies and tools. In the past, use of smartphones for practical reasons has been referred to as ‘productive use’ (or similar), generally referring to use for work purposes. However, today practical or productive use may extend beyond just work purposes, including parenting, social organisation and even legitimate forms of entertainment. To encapsulate th

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Personality Prediction System for Twitter Stream Using Machine Learning Models 1

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6th International Conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Prabakeran, S. Pattanaik, Pratik Chandrasekhar Abinaya, M. Faculty of Engineering and Technology School of Computing Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

Users’ behaviour on social media platforms reflects many aspects of real life, including personality, and these platforms provide a rich amount of textual data. Individuals communicate their thoughts, feelings, and ideas regularly, resulting in a rich and informative collection of personal data that may be used to a wide range of situations. As the word subconscious suggests, this is commonly referred to as "subconscious crowdsourcing," which means that people are unintentionally and subconsciously sharing information with their network. These people are therefore ideal candidates for personality prediction due to the massive expansion of those social media sites over the past ten years. Tweets, for instance, are frequently quite brief and display an odd language usage, consisting of acronyms and unique forms compared to textual sources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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From FOMO to a Legitimate Wellbeing Concern: The Evolution and Impact of Disconnectivity Anxiety According to the Current Research 29

From FOMO to a Legitimate Wellbeing Concern: The Evolution a...

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29th Annual Americas Conference on Information Systems: Diving into Uncharted Waters, AMCIS 2023

作者： Nawaz, Saqib Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

ISBN: (纸本)9781713893592

The increasing prevalence of smartphones and social media has sparked growing concerns about anxiety related to digital disconnection, particularly in the context of problematic smartphone use and dependence (PSUD). Fear of missing out (FOMO) is a widely recognised form of anxiety associated with frequent smartphone use, especially on social media apps, and it has been primarily observed among younger adults. However, beyond FOMO, other technological concerns, such as nomophobia (no-mobile-phone phobia) and technoference (the interference of technology in interpersonal relationships), have also become more prevalent, highlighting the broader impact of smartphone use on individuals' mental wellbeing. My ongoing PhD research into the identification and management of PSUD aimed to delve deeper into understanding the complexities of PSUD and its effects on individuals' lives. FOMO is one of the critical aspects examined within the broader framework of PSUD, as it can have significant implications for mental health and wellbeing. It also explored how FOMO may arise both from PSUD and effectual smartphone use, where individuals may feel concerned about missing important meetings, notifications, calls, or other responsibilities related to their social and professional roles. The multifaceted cause of FOMO was one of the focal points of my investigation, encompassing factors such as the need for connectedness for various reasons and constant exposure to media. I have investigated how FOMO can lead to feelings of inadequacy, loneliness, and anxiety, impacting individuals emotionally and socially. This could include further examining how FOMO may negatively impact self-esteem, increase stress, and contribute to depression and its influence on developing unhealthy coping mechanisms and impulsive behaviours. Addressing FOMO and digital disconnection anxiety within the context of PSUD requires a holistic approach that involves self-care, mindfulness practices, and boundary setti

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