Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

pdf/machine-learning-based-automatic-litter-detection-and-198m6v72.pdf

Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities

Deep-Learning and Blockchain-Empowered Secure Data Sharing for Smart Grid Infrastructure

Sleep is an essential activity that affects an individual’s health and ability to perform activities of daily living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues, such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and nonintrusive environments. This article presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using multiarmed bandit (MAB) methods to 1) learn normal sleep patterns; 2) evaluate sleep quality; and 3) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson sampling (TS), random selection, and upper confidence bound MAB methods. TS outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 h with 85% sleep efficiency and up to three awakenings per night.

Multiarmed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes

The existing EEG emotion classification methods have some problems, such as insufficient emotion representation and lack of targeted channel enhancement module due to feature redundancy. To this end, a novel EEG emotion recognition method (SCLE-2D-CNN) combining scaled convolutional layer (SCLs), enhanced channel module and two-dimensional convolutional neural network (2D-CNN) is proposed. Firstly, the time-frequency features of multi-channel EEG emotional signals were extracted by stacking scl layer by layer. Secondly, channel enhancement module is used to reassign different importance to all EEG physical channels. Finally, 2D-CNN was used to obtain deep local spatiotemporal features and complete emotion classification. The experimental results show that the accuracy of SEED data set and F1 are 98.09% and 97.00%, respectively, and the binary classification accuracy of DEAP data set is 98.06% and 96.83%, respectively, which are superior to other comparison methods. The proposed method has a certain application prospect in the recognition of human mental state.

Channel Semantic Enhancement-Based Emotional Recognition Method Using SCLE-2D-CNN

Drug discovery was initially attributed to coincidence or experimental research. Historically, the traditional approaches were complex, lengthy, and expensive, entailing costly random screening of synthesized compounds or natural products coupled with in vivo validation largely depending on the availability of appropriate animal models. Currently, in silico modeling has become a vital tool for drug discovery and repurposing. Molecular docking and dynamic simulations are being used to find the best match between a ligand and a molecule, an approach that could help predict the biomolecular interactions between the drug and the target host. Beauvericin (BEA) is an emerging mycotoxin produced by the entomopathogenic fungus Beauveria bassiana, being originally studied for its potential use as a pesticide. BEA is now considered a molecule of interest for its possible use in diverse biotechnological applications in the pharmaceutical industry and medicine. In this manuscript, we provide an overview of the repurposing of BEA as a potential therapeutic agent for multiple diseases. Furthermore, considerable emphasis is given to the fundamental role of in silico techniques to (i) further investigate the activity spectrum of BEA, a secondary metabolite, and (ii) elucidate its mode of action.

Computational Applications: Beauvericin from a Mycotoxin into a Humanized Drug

Technological improvements have been exponential in the last two decades. The advancements in networking, fog computing, and artificial intelligence have allowed us to implement the "Internet of Things" starting from small sensors connected to the Internet to enormous "smart cities". However, what is common in all of these improvements is that without data, and more precisely correct data, they would be useless. For example, machine learning training and results will be useless once a malicious or affected transaction is done because it can spread throughout the entire database. The need for data integrity made scholars research different phases of data security such as detection, prevention, and recovery. In this paper, we present an effective damage assessment and recovery algorithm in fog based systems that shortens crucial downtime caused by the recovery process after an attack.

Damage Assessment and Recovery in Fog-based Computing Systems

Abstract Antimicrobial Antimicrobial resistance (AMR), together with extensively drug resistant (XDR), mainly among Gram-negative bacteria, has been on the rise. Colistin (polymyxin E) remains one of the primary available last resorts to treat infections by XDR bacteria with the rapid emergence of global resistance. Since the exact mechanism of bacterial resistance to colistin remains unfolded, this study warranted elucidating the underlying mechanism of colistin resistance and heteroresistance among carbapenem-resistant (CR) Klebsiella pneumoniae isolates. Molecular analysis was carried out on the resistant isolates using a genome-wide characterization approach, and MALDI-TOF MS for lipid A. Among the 32 CR K. pneumoniae isolates, three and seven isolates showed resistance and intermediate resistance, respectively, to colistin. The seven isolates with intermediate resistance exhibited the “skip-well” phenomenon, attributed to the presence of resistant subpopulations. The three isolates with full resistance to colistin showed ions using MALDI-TOF MS at m/z 1840 and 1824 representing bisphosphorylated and hexaacylated lipid A with or without hydroxylation, at position C’-2 of the fatty acyl chain, respectively. Studying the genetic environment of mgrB locus revealed the presence of insertion sequences that disrupted the mgrB locus in the three colistin resistant isolates: IS1R and IS903B. Our findings showed that colistin resistance/heteroresistance was inducible with mutations in chromosomal regulatory networks controlling lipid A moiety and IS sequences disrupting the mgrB gene leading to elevated MIC values and treatment failure. IS monitoring in K. pneumoniae could help prevent the spread of colistin resistance and decrease colistin treatment failure. 

Genetic and Structural Basis of Colistin Resistance in Klebsiella pneumoniae: Unraveling the Molecular Mechanisms

Leishmaniasis is a neglected tropical disease infecting the world's poorest populations. Miltefosine (ML) remains the primary oral drug against the cutaneous form of leishmaniasis. The ATP-binding cassette (ABC) transporters are key players in the xenobiotic efflux, and their inhibition could enhance the therapeutic index. In this study, the ability of beauvericin (BEA) to overcome ABC transporter-mediated resistance of Leishmania tropica to ML was assessed. In addition, the transcription profile of genes involved in resistance acquisition to ML was inspected. Finally, we explored the efflux mechanism of the drug and inhibitor. The efficacy of ML against all developmental stages of L. tropica in the presence or absence of BEA was evaluated using an absolute quantification assay. The expression of resistance genes was evaluated, comparing susceptible and resistant strains. Finally, the mechanisms governing the interaction between the ABC transporter and its ligands were elucidated using molecular docking and dynamic simulation. Relative quantification showed that the expression of the ABCG sub-family is mostly modulated by ML. In this study, we used BEA to impede resistance of Leishmania tropica. The IC50 values, following BEA treatment, were significantly reduced from 30.83, 48.17, and 16.83 µM using ML to 8.14, 11.1, and 7.18 µM when using a combinatorial treatment (ML + BEA) against promastigotes, axenic amastigotes, and intracellular amastigotes, respectively. We also demonstrated a favorable BEA-binding enthalpy to L. tropica ABC transporter compared to ML. Our study revealed that BEA partially reverses the resistance development of L. tropica to ML by blocking the alternate ATP hydrolysis cycle.

ABC transporter inhibition by beauvericin partially overcomes drug resistance in Leishmania tropica.

Early SARS-CoV-2 variant detection relies on testing and genomic surveillance. The Omicron variant (B.1.1.529) has quickly become the dominant type among the previous circulating variants worldwide. Several subvariants have emerged exhibiting greater infectivity and immune evasion. In this study we aimed at studying the prevalence of the Omicron subvariants during the flu season and beyond in Lebanon through genomic screening and at determining the overall standing and trajectory of the pandemic in the country. 

Tracking SARS-CoV-2 variants during the 2023 flu season and beyond in Lebanon

Popular Tasks used by Researchers

How can I help with your research?

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

Anyone with the link can view this chat. You can switch it back to private anytime.

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sergio Thoumi

Author Tools

Chat about Author