Deep Web Research Articles

Retraction Note: Efficiently harvesting deep web interfaces based on adaptive learning using two-phase data crawler framework

Retraction Note: Efficiently harvesting deep web interfaces based on adaptive learning using two-phase data crawler framework

Dark Web and Its Ramifications

The dark web, a hidden arena of the internet accessible only through specialized software. This intellectual on growing crime is rising day by day. This paper will throw a light on the negative effects of the dark web on the Indian society in the fields of like digital transactions involving money, human trafficking, private forums, online examinations and other sensitive information make up the deep web. Despite existing laws and regulations governing cybercrime and online activities, law enforcements agencies in India encountering significant challenges in investigating and prosecuting crimes committed via dark web technologies.

Cryptocurrency and its Nexus with Money Laundering and Terrorism Financing within the Framework of FATF Recommendations

Cryptocurrency has emerged as a viable alternative to conventional payment systems, offering its users notable advantages such as cost efficiency and rapid transaction processing. However, cryptocurrencies’ decentralized nature, anonymity, and susceptibility to cyber threats introduce the potential for their exploitation in illicit activities, notably money laundering and terrorism financing (ML/TF). This scholarly investigation seeks to investigate the roles played by international organizations actively combating such criminal activities. In this academic context, a meticulous consideration of the inherent risks associated with the aforementioned criminal endeavors is undertaken, aligning with the guidelines and recommendations set forth by the Financial Action Task Force (FATF). The study underscores a significant finding that cryptocurrency accounts can be opened anonymously, with no centralized registry to monitor cryptocurrency ownership. This characteristic poses a formidable challenge in confiscating funds linked to terrorist activities. Even in instances where cryptocurrency transactions may be traced, accessing this critical data necessitates the involvement of third-party entities, given that cryptocurrency transactions are recorded in decentralized ledgers spanning multiple jurisdictions. Furthermore, the study underscores the imperative of coordination and information exchange as indispensable elements in the ongoing battle against organized and transnational criminal activities, specifically ML/TF. Moreover, the study elucidates the incongruence between cryptocurrency and FATF regulations governing electronic fund transfers, as cryptocurrencies can make transactions through opaque and unregulated conduits, such as the deep web.

Adolescent HIV prevent and care framework: A global scoping review protocol- BSGH 006.

Among adolescents, HIV/AIDs remains a significant cause of death globally [1-4]. Given the unique stages in human development, adolescents have been shown to fall within a sexually active phase. Combined with other social and structural factors in their immediate environments, HIV prevention and care among adolescents can be filled with challenges for intervention. This paper outlines this protocol to systematically review peer-reviewed literature in prevention and care among adolescents 10-19 years. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) will be used to report this scoping review. The review will involve screening and extracting data using covidence as the primary tool. The review will encompass quantitative, qualitative, and mixed methods studies, utilizing a search strategy from electronic databases such as PubMed (NCBI), Web of Science Core Collection (Clarivate), Embase (Elsevier), and Scopus (Elsevier). Additionally, a search will be conducted for grey literature using Global Index Medicus (WHO), MedNar (Deep Web Technologies), and Central Register of Controlled Trials (Cochrane). Duplicate removal and selection of articles that meet the inclusion criteria for the study will be performed using Covidence. Once the screening process is complete, data will be extracted from the full-text screened articles in Covidence. We will pilot the extracted data in Covidence to ensure that all relevant information has been captured, making necessary changes if required. Data extraction will be carried out by at least two authors, with any conflicts resolved by the same authors. If a conflict cannot be resolved between the two, a third author will make a final determination. We aim to analyze data thematically by employing a grounded theory approach to generate codes pertinent to the research question. The team will review and discuss codes to create a cohesive set of codes that will be instrumental in identifying knowledge gaps and constructing themes that summarize the data. The proposed systematic review will be among the pioneering efforts to rigorously assess global data on HIV prevention and care, with a specific focus on adolescents 10-19 years. It will consider the diverse socio-economic factors and experiences shaping these adolescents' lives in HIV prevention and care. We expect this review to yield critical insights into the present landscape of HIV prevention and care for individuals aged 10-19. These findings will also play a pivotal role in shaping the development of a global framework that researchers and stakeholders can readily adopt and implement across socio-economic contexts. This framework will aim to address the unique needs of all adolescents concerning HIV prevention and care.

SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions

Mature microRNAs are short, single-stranded RNAs that bind to target mRNAs and induce translational repression and gene silencing. Many microRNAs discovered in animals have been implicated in diseases and have recently been pursued as therapeutic targets. However, conventional pharmacological screening for candidate small-molecule drugs can be time-consuming and labor-intensive. Therefore, developing a computational program to assist mature microRNA-targeted drug discovery in silico is desirable. Our previous work (https://doi.org/10.1002/advs.201903451) revealed that the unique functional loops formed during Argonaute-mediated microRNA-mRNA interactions have stable structural characteristics and may serve as potential targets for small molecule drug discovery. Developing drugs specifically targeting disease-related mature microRNAs and their target mRNAs would avoid affecting unrelated ones. Here, we presented SMTRI, a convolutional neural network-based approach for efficiently predicting small molecules that target RNA secondary structural motifs formed by interactions between microRNAs and their target mRNAs. Measured on three additional testing sets, SMTRI outperformed state-of-the-art algorithms by 12.9-30.3% in AUC and 2.0-18.4% in Accuracy. Moreover, four case studies on the published experimentally validated RNA-targeted small molecules also revealed the reliability of SMTRI. Our method is available as a user-friendly web server at http://smtri.net.

Maternal asthma during pregnancy and risks of allergy and asthma in progeny: A systematic review and meta-analysis.

Clinical and preclinical evidence indicate that in utero maternal asthma exposure increases progeny asthma risk. Whether maternal asthma also increases the risks of progeny allergy is unclear. To synthesise the available evidence on the relationship between in utero exposure to maternal asthmaand postnatal asthma, wheezing and allergic diseases (Prospero: CRD42020201538). We systematically searched MEDLINE [PubMed], Embase [Ovid], Web of Science, Informit Health, the Cochrane Library, CINAHL [EBSCOhost], MedNar [Deep Web Technologies], ProQuest Theses and Dissertations, Scopus [Elsevier] and Trove, to the end of 2023. Studies reporting asthma, wheeze and/or allergic disease in progeny of women with and without asthma or with asthma classified by control, exacerbation or severity. Double screening, selection, data extraction and quality assessment were performed, using Joanna Briggs Institute (JBI) scoring. Of 134 non-overlapping studies, 127 were included in ≥1 meta-analysis. Maternal asthma ever was associated with greater risks of asthma (65 studies, risk ratio [95% confidence interval] 1.76 [1.57-1.96]), wheeze (35 studies, 1.59 [1.52-1.66]), food allergy (5 studies, 1.32 [1.23-1.40]), allergic rhinitis (7 studies, 1.18 [1.06-1.31]) and allergic dermatitis (14 studies, 1.17 [1.11-1.23]) ever in progeny. Asthma during the pregnancy, more severe, and uncontrolled maternal asthma were each associated with greater risks of progeny asthma. Children of mothers with asthma are at increased risk for the development of allergic diseases. Whether improved maternal asthma control reduces risks of child allergy as well as asthma requires further investigation.

Application of High-Dimensional Data Visualization and Visual Communication Technology in Virtual Reality Environment

How to efficiently find what you want from massive data has become a hot topic. It is precisely because network information is developing so fast that so many data-oriented software systems and pages are used to manage so much network information. Aiming at problems such as insufficient information mining of visual communication websites in ASPNET and poor visual communication effects, a design scheme for integrating high-dimensional data into visual communication websites is proposed. Its working architecture includes high-dimensional data integration, multi-type visual integration, and visual support services. The main functions of the website include comprehensive management, charts, data sources, projects, systems, etc. We have conducted specific design and Implementation of high-dimensional information integration on the website, completed the design and Implementation of integration of high-dimensional data, integration of multiple visualization types, and integrated access to visualization-supported functions. Use a deep web crawler to search the visual site. It uses a search-based approach to filter URLs and crawl valuable web pages. Practice has proved that the intelligent distribution network management page implemented by the solution proposed in this article has good performance. The system shows good performance in visualization implementation and the ability to discover information. The system achieved sound and visual communication effects.

Identification of inhibitors for neurodegenerative diseases targeting dual leucine zipper kinase through virtual screening and molecular dynamics simulations

ABSTRACT Neurodegenerative diseases lead to a gradual decline in cognitive and motor functions due to the progressive loss of neurons in the central nervous system. The role of dual leucine zipper kinase (DLK) in regulating stress responses and neuronal death pathways highlights its significance as a target against neurodegenerative diseases. The non-availability of FDA-approved drugs emphasizes a need to identify novel DLK-inhibitors. We screened NPAtlas (Natural products) and MedChemExpress (FDA-approved) libraries to identify potent ATP-competitive DLK inhibitors. ADMET analyses identified four compounds (two natural products and two FDA-approved) with favourable features. Subsequently, we performed molecular dynamics simulations to examine the binding-stability and ligand-induced conformational dynamics. Molecular mechanics Poisson Boltzmann surface area (MM-PBSA) calculations demonstrated CID139591660, dithranol, and danthron having greater affinity, while CID156581477 showed lower affinity than control sunitinib. PCA and network analysis results indicated structural and network alteration post-ligand binding. Furthermore, we identified an analogue of CID156581477 using the deep learning-based web server DeLA Drug which demonstrated a higher affinity than its parent compound and the control and identified several crucial interacting residues. Overall, our study provides significant theoretical guidance for designing potent novel DLK inhibitors and compounds that could emerge as promising drug candidates against DLK following laboratory validation.

Feasibility of attacks on hidden web databases by partially informed external attackers

Feasibility of attacks on hidden web databases by partially informed external attackers

Overcoming deep web challenges: Sustainable solutions for digital age information seekers

In the modern digital landscape, the deep web presents itself as a hidden realm containing a wealth of concealed knowledge beneath the surface web. This paper extensively examines the challenges associated with the deep web, elucidating its lack of indexation, access barriers, and ethical considerations. It also provides insights into effective strategies for navigating this domain, including advanced search methods, specialized search engines, and collaborative efforts. Furthermore, the paper explores the fusion of technology, education, and open-access initiatives as sustainable approaches for contemporary knowledge seekers. The utilization of technology, encompassing techniques like web scraping and data interpretation, emerges as a central aspect of accessing hidden insights. Nevertheless, this is complemented by digital literacy and ethical consciousness to ensure responsible exploration. Collaborative partnerships between technological pioneers and information experts facilitate wider access to deep web resources. In conclusion, the study emphasizes the importance of comprehending the complexities of the deep web to tap into its extensive knowledge reserves, ultimately empowering modern information seekers of knowledge.

Optimizing Data Extraction using Preprocessing for Enhanced Efficiency

With the exponential growth of the internet, an abundance of information has become readily available. Extracting valuable data from the web is crucial for applications such as meta-querying and comparison shopping. However, the heterogeneous nature of web information poses a significant challenge to the extraction process. The web can be classified into the surface or visible web and the deep or invisible web. While conventional search engines can index the surface web, they fall short when it comes to the deep web.
 To access the deep web, users must submit queries to web databases, and the results are encapsulated in dynamically generated web pages containing data records. Traditional search engines struggle to index these dynamic pages, necessitating a specialized program for efficient information extraction from the deep web. Web search engines generate result pages based on user queries, making it crucial to automatically extract data from these pages for various applications.
 In this context, we propose an innovative data extraction method called Effective Data Extraction using Preprocessing (EDEP). The EDEP approach begins by parsing the input HTML page, constructing a tag tree, and subsequently eliminating irrelevant tags from the tree. Notably, our system efficiently handles scenarios where auxiliary information, such as recommendations or comments, is intermixed between query result records (QRRs), causing them to be non-contiguous. EDEP also effectively manages result pages containing single QRRs.
 Through experimental results, it is evident that EDEP outperforms existing data extraction methods, showcasing its efficacy in handling the complexities associated with web data extraction.

A Lightweight Deep Learning Model and Web Interface for COVID-19 Detection Using Chest X-Rays

A Lightweight Deep Learning Model and Web Interface for COVID-19 Detection Using Chest X-Rays

Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models

Deep Learning and Web Applications Vulnerabilities Detection: An Approach Based on Large Language Models

The Web Layers: Security Challenges and Solutions in Surface, Deep and Dark Web

The Web Layers: Security Challenges and Solutions in Surface, Deep and Dark Web

Pterygium axillae in association with Poland Syndrome

We report the case of a 2-year-old boy with a left-sided axillary pterygium and concomitant Poland’s Syndrome. The Pterygium presented as a deep web extending from the lateral thorax to the lower arm, severely restricting shoulder motion. Following excision of the entire fibrous membrane and multiple Z-plasty release of the skin, shoulder movement was greatly improved. We speculate that the pterygium may represent anlage from the absent pectoralis muscle.

Ethical Considerations for Open-Source Investigations into International Crimes

Over the past decade, the field of digital open-source investigations has both expanded and matured. Open source investigations rely on information that anyone can access from the Internet, and have been increasingly deployed by legal investigators, human rights researchers, and journalists. The investigatory methods include Boolean searches; sourcing videos, photographs, and other data from social media; determining locations of events by comparing photos and videos to satellite images; mining the deep web for government records; determining time of day by analyzing shadows in photos; and more. In this essay, I argue that digital open-source investigators can (and should) rely on a three-step process when faced with an investigations-related dilemma: (1) identifying what the law says they can or cannot do according to their professional identity, intended purpose, and relevant jurisdiction; (2) soliciting guidance from their professional code of ethics (if they have one); and (3) weighing their options against the values of safety, accuracy, and dignity. Given space constraints and prior coverage of the first two steps, I focus here on step three. The values of safety, accuracy, and dignity arguably apply to all open-source investigations and investigators, regardless of jurisdiction or professional identity, and their application reflects a relatively novel attempt to help set ethics-based boundaries around investigation-related activities.

Sömürgeciliğin İkiyüzlülüğü: This Earth of Mankind Adlı Romanda Irkçılık ve Kimlik Kavramları

This Earth of Mankind, by Pramoedya Ananta Toer, is a moving literary masterpiece that looks into the complexity of identity and the lingering impacts of colonial subjugation. The novel's analysis of oppression reveals the deep web of power relations inherent in colonial countries. The novel illuminates the enormous inequalities between the colonizers and the colonized, as well as the racial and socioeconomic hierarchies that sustain the systemic oppression of the indigenous population. It follows Minke, a Javanese native protagonist, as he struggles with his multiple identities as an educated man finding his way through Dutch-dominated society and as a representation of his indigenous culture. In this regard, this article emphasizes the varied nature of identity creation in a colonial situation through an in-depth investigation of major characters and their relationships. Additionally, the article focuses on how the authoritarian colonial system impacts and constrains the identities of other individuals in the novel. Drawing upon postcolonial theories and historical context as well, this article also sheds light on the recurring themes of identity negotiation, cultural assimilation, and resistance within This Earth of Mankind. It aims to foster a better understanding of the psychological, social, and cultural impacts of colonialism on both the colonizers and the colonized. Ultimately, this article aims to contribute to the ongoing discourse on decolonization, identity politics, and the legacy of colonialism through an in-depth analysis of Toer's masterpiece.

Digital Depth: A Volumetric Speculation

Counter commonplace associations with superficial mediation and networked flatness, the digital seems to have its own peculiar depths, which range from the infrastructural (deep sea cables, deep packet inspection, crawl depth) to the metaphorical (Deep Web, deep learning, deepfakes). This article reviews recent discussions of digital depth and argues that this concept is central to understanding multiple aspects of digital media ranging from folk theorizations to technical expertise. What is digital depth? What is deep about digital media? How does this depth interface with volumes and scales beyond the digital? Through this effort, depth emerges as an underlying feature of deeply mediatized societies.

Implications of a local flora survey for pre‐human grass flora in north‐western Madagascar

Societal Impact StatementMadagascar's children are taught that their ancestors destroyed the vast ancient forest, giving rise to modern grasslands, a misleading myth that continues to undermine people's relationship with nature. Replacing this myth with a more nuanced and accurate narrative around grassy ecosystems is fundamental to building a more positive relationship between Madagascar's people and ecosystems, a process that is necessary to build modern conservation practice and environmental governance. The data we present are consistent with a pre‐human grass flora in the region, suggesting the local grassy ecosystems should not be written off as fully anthropogenic and undesirable but are instead in need of recognition and further research attention.Summary Grassy ecosystems cover over a quarter of dry land, with mosaics of old‐growth and recently formed anthropogenic systems appearing superficially similar to an uninformed observer. Grasses are part of a deep web of historic relationships between grassy ecosystems and local people. We collect and compile the first list of grass species (Poaceae) in north‐west Madagascar in order to make an initial evidence assessment for a pre‐human grass flora. Field surveys and voucher specimen collections were carried out. Specimen identification was performed using morphological character comparison with reference specimens, literature and personal knowledge of grasses from other parts of Madagascar. Seventy species are recorded and assigned to 44 genera. The species list is compared with regional grass checklists from central and south‐central Madagascar. Thirty‐five grasses are unique to Mahajanga, comprising 19% of the multi‐region species pool analysed; the regional species richness is comparable to that of the highlands. The main subfamilies represented are the Panicoideae (64%) and Chloridoideae (29%). Seven species are endemic to Madagascar, reflecting their evolutionary origins on the island. Many of the species are found in multiple types of habitat and substrate, while others are restricted to limited areas and habitats, and local species richness varies significantly with habitat and soil. The main disturbance regimes driving species occurrence include fire and grazing, possibly partly reflecting similar disturbances prior to human arrival. The patterns observed in the grass flora of north‐western Madagascar are overall congruent with a pre‐human grass flora.

Kathakali character identification – Using deep learning techniques and web technologies for Indian cultural heritage

Technology advancements have proven to benefit every part of our life. It has shown visible results in all domains including medicine, retail, governance, entertainment to quite a few. There are several ways technology has been used for art and culture. In this paper, we examine the use of technology for a 400 year old art form Kathakali, one of the most sacred arts of Indian Cultural Heritage. Kathakali is known for hand-gestures, dance elements, music and sophisticated costumes and makeups. Kathakali is salient part of Indian Cultural Heritage. However, there haven't been much work to promote this part of Indian history. Through this paper, we aim towards taking one major step between our technological field and Indian cultural heritage. We use artificial intelligence (AI) techniques such as convolutional neural networks for Kathakali character identification from images. We employ different deep learning techniques for the same and evaluate the performance of our algorithms. We observe that through rigorous parameter tuning and experiments, up to 97.5% accuracy has been obtained. We have personally recorded images of Kathakali from live performances over 20 years. The data set developed for this study contains images for 6 Kathakali characters - Pacha, Kathi, Minukku-Female, Red Beard, White Beard and Kari-Male and have been made available for the public to use in future. We then develop a web platform wherein users can upload new Kathakali images and the platform uses the AI algorithm behind to identify its character type. The web platform has been designed to deploy in a cloud environment so that it can benefit the public. We show how technology advancements can help improve audience reach for such old Indian art forms through this paper.