bluuebunny/crossref_metadata_2025 huggingface dataset:
Misinformation Knowledge Synthesis: Using Meta-Analysis to Develop Interdisciplinary Interventions
Authors: Byun Mimi , Rashid Mohotarema
Published: October 2024

Due to the disciplinary silo effect of academic research, there is the need to consolidate and synthesize theories for misinformation from multiple disciplines. While the call has been made for the integration necessary to qualify interdisciplinary research, there has been little progress nor substantial efforts made toward this goal. Current challenges include departmental dynamics, the lack of training and resources to support interdisciplinary research, or pressures made by the tenure-track promotion system. Therefore, we propose a method of consolidating top theories and frameworks derived from reviews of scholarly literature from multiple disciplines. This would require a systematic review of systematic reviews encompassing misinformation and its relevant derivations, being mindful that coming to a consensus using terminology remains one of the chief challenges of interdisciplinary work. In addition to identifying the top theories used in research, an overview of the epistemological origins as well as a relevant application could be used as a visual framework to bridge the vast gulfs dividing each school of thought. Another way to discover what disciplines are in collaboration is to visualize the social network connections formed through co-citation analysis. With this method of meta-analysis and subsequent synthesis, more comprehensive understandings will develop, improving both the automated detection methods for misinformation and interventions to mitigate its effects.
Digital Language Archiving: Reuse and Adaptation of Non-LIS Learning Materials in LIS Education
Authors: Coronado Sergio , Zavalina Oksana
Published: October 2024

As a digital repository type that is gaining prominence, language archives are in need of information professionals to maintain and provide access to. Yet, awareness of digital language archives among the US LAM professionals and educators is currently low. Our project’s analysis of available relevant training materials for linguists and community members informed development of a graduate course that prepares LAM professionals to effective and ethical stewardship of digital language archives. This paper provides a brief literature review of the latest relevant research and publications. Next, results of examination of three sources of language archiving learning materials are presented and discussed. This discussion’s focus is on the ideas for reuse and adaptation of these materials in LAM education and on implementation of some of these ideas in our project-developed course at the University of North Texas.
Digital Language Archiving: Reuse and Adaptation of Non-LIS Learning Materials in LIS Education
Authors: Coronado Sergio , Zavalina Oksana
Published: October 2024

As a digital repository type that is gaining prominence, language archives are in need of information professionals to maintain and provide access to. Yet, awareness of digital language archives among the US LAM professionals and educators is currently low. Our project’s analysis of available relevant training materials for linguists and community members informed development of a graduate course that prepares LAM professionals for effective and ethical stewardship of digital language archives. In this paper, following a brief literature review of the latest relevant research and publications, results of examination of three major sources of language archiving learning materials are presented and discussed.  This discussion’s focus is on the ideas for reuse and adaptation of these materials in LIS education and on implementation of some of these ideas in our project-developed course at the University of North Texas.
Understanding Ourselves in Qualitative Research Through Collaborative Sense-making
Authors: Crooks Shannon , Sarah Appedu  , Abir Bouazizi  , Ingrid Erickson  , Chien-Yu Lin  , Qiaoyi Liu  , Isaac Meth  , Yigang Qin
Published: October 2024

In the fall of 2023, the iSchool at Syracuse University offered a new PhD course entitled IST 800 Information Studies Seminar: Qualitative Research Methods. This course was intended to teach students about various qualitative methods. A key part of the curriculum was centered on a discussion of how researchers are implicated by their epistemological and ontological positionality and how this awareness should inform their methodological decisions and analytical interpretations. As part of the course, students were asked to keep a weekly sense-making journal, which was intended as a routine medium through which students could reflectively engage with the  various author claims, arguments, and philosophical perspectives that were raised in the course readings and shared class discussions. After the course ended, several of the students from the course embarked on a collaborative autoethnographic project to examine how their respective learnings about qualitative methodologies had shaped their individual research ontologies and epistemologies. This poster provides an overview of this collective analysis. 
 
We argue that the reflexive nature of the course acted as a generative springboard for us (i.e., the students) to explore and question our values, sensitivities, and, ultimately, our worldviews; in tandem, the assigned sense-making memos gave us a chance to proactively develop our researcher positionalities.  It is our hope that sharing the insights from this autoethnographic project will support other early career researchers in understanding how to critically engage their own positionality, and will provide information science educators inspiration for their own research methodology pedagogy.
Forward Together: Working with the New Standards for Accreditation
Authors: Adkins Denice , Copeland Andrea J. , O'Connor Lisa , Price Kerri
Published: October 2024

The American Library Association Committee on Accreditation (COA) will host an interactive session on working with the newly approved Standards for Accreditation of Master’s Programs in Library and Information Science (2023).
The 2023 Standards were adopted by approval of the Council of the American Library Association (the Council) on November 30, 2023. These revised Standards eliminate redundancies, improve clarity, and infuse concepts of Equity, Diversity, and Inclusion throughout the standards. The collaborative process that supported this work will ensure that these revised standards support a responsive, 21st century library and information education rooted in the fundamental principles of the profession as espoused by the American Library Association. The 2023 Standards take effect for all programs with comprehensive review visits scheduled for Spring 2026 or afterward, and programs with comprehensive review visits in 2025 may choose to respond to the new standards if they wish.
COA members will discuss the structure and intent of the 2023 Standards and how those can be applied across diverse programs. Panelists will discuss how programs with reviews after 2025 might transition from the 2015 to 2023 standards. Additionally, the panel will share their thoughts on types of evidence sufficient to meet the needs of the new standards and reporting expectations for those standards in program statistical reports, biennial narrative reviews, and comprehensive reviews.
Finally, attendees and panelists will discuss opportunities and challenges in responding to the new standards. Attendees and panelists will discuss strategies for reporting on the standards to demonstrate that the program embraces the values of the LIS professions while also taking into account those programs’ local and regional contexts.
Diversity, Equity, Inclusion, and Access Coverage in Technology-focused Courses
Authors: Lofca Izzet
Published: October 2024

Diversity, equity, inclusion, and access (DEIA) is a controversial topic. Technological improvements help to incorporate DEIA to everyday life, but it also comes with its own problems like algorithmic bias, and legal issues. Computing technology is not neutral; adversely affecting marginalized groups through policy decisions and their creators’ unconscious biases; the limitations of the programs and equipment. The American Library Association Core Competences (ALACCs) require basic knowledge on social justice, diversity, equity, and inclusion gained through LIS education. This study will analyze technology-focused Master of Library and Information Science (MLIS) courses to assess how DEIA principles are addressed in the light of ALACCs in technology requirement. Syllabi from select ALA accredited MLIS programs on the Eastern United States are collected. Preliminary results will be presented at the Conference. Efforts to collect the remaining syllabuses are continuing. Study hopes to fill a major gap by providing a concrete data about technological biases that inversely affect specific populations as well as possible remedies to it.
“What is all the damage the drugs are doing to my body?” A study of the effect of internalized stigma on harm reduction information-seeking behavior of people who use opioids in Kensington
Authors: Sullivan Zimmerman Margaret , Oh Sein
Published: October 2024

The United States is in the midst of an opioid crisis. The rate of overdose deaths rose 30% from 2019 to 2020, numbers that represent an escalation theorized to have been brought on by the COVID-19 pandemic. Over 112,000 people died of drug overdoses, mainly from synthetic opioids, like fentanyl, in 2023 (CDC, 2024).
This poster will display the results of a mixed-methods survey conducted with 101 people who use opioids in the Kensington neighborhood of Philadelphia- commonly called the epicenter of the opioid crisis. Participants were asked about their experiences with getting harm reduction information and how those experiences interacted with their own feelings of stigma. They were asked about their harm reduction information needs, experiences looking for this information, and how best to provide it to them.
This poster will display current unpublished results to-date. The data analysis has revealed a meaningful impact on information-seeking behavior for participants who have heightened internalized stigma- even to their known detriment. This study is meaningful to stakeholders, including librarians, who may provide public health information to the community of people addicted to opioids. And, to the conference theme, what is more ethical that providing harm-reduction information to the addicted?
LIS Curriculum Coverage of Sustainability
Authors: Ren Xiaoai
Published: October 2024

The Association for the Advancement of Sustainability in Higher Edcuation calls for higher education to be effective change agents and drivers of sustainability innovation. The International Federation of Library Associations and Institutions has advocates for libraries’ unique and important contributions to the global sustainable development efforts (2016). In 2019, the American Library Association added sustainability as a core value of librarianship. Under this environment, LIS publications on libraries and sustainable development identified a lack of the awareness and knowledge of sustainability among librarians and called for it to be covered in LIS curriculum. This study explores the coverage of sustainability in LIS curriculum and aims to explore how LIS profession understands sustainability, whether sustainability should be in LIS curriculum, and what competences it develops in LIS students. Findings from this study will further the discussion on libraries’ roles in society and how to equip LIS professionals with needed knowledge and skills to act on those roles.
Investigating Mobility, Place, and Information Access Through an Ethnographic Study of Vehicle Residents’ Information Practices in the United States
Authors: Montague Kaitlin
Published: October 2024

Throughout the United States, an approximated one million people currently inhabit vehicles parked in publicly accessible areas as their primary method of housing. This dissertation explores vehicle residents’ information seeking practices and barriers to information access. Library and Information science (LIS) research has not developed theory related to place, mobility, and information access, despite living in a hyper-mobile world. LIS primarily centers online information behaviors, limiting potential impact on larger populations that face unique barriers to information access. While some studies constructed theory to address the function of place in information behavior, few interrogate how place and mobility impact information access despite geographic mobility serving important functions in various theory constructions. To address the importance of place and mobility in the context of information access, I conducted a multi-method, ethnographic study. Completing two rounds of deeply embedded fieldwork from June-October 2023 in Santa Cruz, California (CA) and from January-February 2024 along the Southern CA and Arizona (AZ) border, I used participant observation, information horizon interviews, and guided tours and photographs of participant’s vehicles to explore vehicle residents’ information practices and information source preferences. I determined my key findings in Spring 2024; they reveal that participants experience micro-fractures in their information landscapes when moving through new information environments. Identifying vehicle residents’ information practices and most used information sources can help public libraries and other information institutions implement improved support services for this growing population as public libraries are well positioned to bolster information access on behalf of its communities.
INVESTIGATION OF DEEP LEARNING OPTIMIZERS FOR FALSE WINDOW SIZE INJECTION ATTACK DETECTION IN UNMANNED AERIAL VEHICLE NETWORK ARCHITECTURE
Authors:   Avinashilingam Institute for Home Science and Higher Education for Women, India, N Vanitha , G Padmavathi ,   Avinashilingam Institute for Home Science and Higher Education for Women, India
Published: October 2024

The Unmanned Aerial Vehicle (UAV) network plays a prominent role in this pandemic era. Nowadays UAVs are applied in various applications like military, civil etc. This article works on the Search and Rescue application part. UAV networks are applied in search and rescue operations in order to find the missing people in Hill areas. Due to false data dissemination attacks some UAVs in the network will lost the data so the rescue will become an issue. In order to detect those attacks this work uses Feed Forward Neural network with backpropagation algorithm. This work experiments chosen optimizers to get the accurate detection of attack and compares the results among the optimizers All the more explicitly this examination did in the Delay-Tolerant based Decentralized Multi-Layer UAV ad-hoc organization Assisting VANET (DDMUAV) design utilizing Opportunistic Network Environment (ONE) test system.
A RELIABLE FAST AND IDEAL TRAFFIC AWARE SECURE ROUTING PROTOCOL BASED ON MULTI-HOP CROSS SITE LEAPING FOR EFFICIENT PACKET TRANSMISSION IN WSN
Authors:   Thiruthangal Nadar College, India, Raja A Ambeth , Kumar A Udhaya ,   Agurchand Manmull Jain College, India
Published: October 2024

Fast Transmission in Wireless medium possess on node selection routing and unicasting the wireless sensor networks of the traditional secure aggregation system, the node broadcast it to all its neighbors as compared to what it contained. However, due to the rigorous network environment surrounding the nodes, many researchers usually take a different approach from traditional networks. Wireless sensor networks (WSN) are currently used in a variety of applications and will be used in more applications in the future. However, wireless sensor networks tend to be more unreliable than wired networks. In this paper, proposes A Reliable fast and ideal traffic aware secure routing protocol (RTASR) based on multi-hop cross site leaping for efficient packet transmission in WSN. Group-based fast proximity detection algorithm (GBFA) solves these problems. By bearing neighboring information in the beacon pocket, the node knows some potential neighbors in advance. This allows fast discovery to pick up the most energy-efficient neighbors to speed up energy efficiency and reduce network communication load, and actively nodes to verify that this dynamic neighbor is a true neighbor. The simulated results show that the proposed scheme reduces the loss of bandwidth in comparison to routing mechanisms that extend the life of the network.
COMPARATIVE STUDY OF EVOLUTIONARY ALGORITHMS TO GENERATE FLAT-TOP BEAM PATTERN FOR SYNTHESIS OF A LINEAR ANTENNA ARRAY
Authors:   Oriental University, India, Patidar Hemant , Maheshwari Vikas ,   Guru Nanak Institutions, India, Kar Rajib ,   National Institute of Technology, Durgapur, India, Singh Prasanna Kumar ,   Noida Institute of Engineering and Technology, India, Sahu Vijay Kumar ,   Anil Neerukonda Institute of Technology and Sciences, India
Published: October 2024

In the present paper three evolutionary algorithms are compared and discussed for synthesizing a linear array of dipole antenna of half-wavelength oriented horizontally. All the dipoles are mutually coupled and designed to radiate the flat-top beam (FTB) pattern including multiple single null steering with little ripple deviation. The adopted evolutionary algorithms are; flower pollination algorithm (FPA), firefly algorithm (FA) and back tracking search algorithm (BSA), respectively. These algorithms are then combined with an efficient Inverse Fast Fourier Transform (IFFT) for reduction in the computation of evaluating time meaningfully. The required synthesis is attained by generating the current amplitudes at 00 and 1800 using binary phase shifters for the antenna array. The performance analysis of key antenna parameters along with the statistical parameters is achieved with the help of the optimization process and compared. In this paper, the MATLAB tool is used as a validation tool and the obtained simulation results are very satisfying and acceptable.
A COMPARATIVE STUDY ON INTRUSION DETECTION SYSTEMS FOR SECURED COMMUNICATION IN INTERNET OF THINGS
Authors:   P.K.R. Arts College for Women, India, R Anushiya , V S Lavanya ,   P.K.R. Arts College for Women, India
Published: October 2024

The virtual and physical worlds are bridged using the largest digital mega-trend called the Internet of Things (IoT). Between mankind, new interactions and new business models are emerging due to the incremental growth in the Internet, machines, objects, and people connectivity. Secured communication is a typical challenge that is raised due to IoT high diversity, restricted computational resources, and protocols and standards. Because of the huge attack surface in IoT networks, they are highly vulnerable to various attacks, even with some security measures. So, for detecting attacks, it is necessary to design defense mechanisms. In IoT environments, it is highly crucial to have security defense measures like Intrusion Detection Systems (IDS). Hence, authentication and encryption traditional security countermeasures are not sufficient. At network level, to solve those issues and to protect Internet-connected frameworks, major solutions are provided by IDS. Highly unique challenges are faced by IoT specific characteristics like malware detection, ransomware, processor architecture heterogeneity, and the gap in security design. However, as in literature, various problems are raised in traditional IDS, like the high false alarm rate. In IoT, for intrusion detection, a detailed study of traditional Deep Learning (DL) and Machine Learning (ML) techniques and recent technologies is presented in this review. For presenting every selected work objective and methodology, they are analysed and this review work discusses their results. IoT systems cannot be secured by applying traditional security techniques directly due to their computational constraints and intrinsic resources. In real time, on IoT devices, unknown and known attacks are detected using ML techniques in IDS. An IDS is presented in this review and its working is independent of network structure and IoT protocols. This IDS do not require any prior knowledge of security threats. Therefore, for providing security as a service to IoT networks, an artificially intelligent IDS is developed. This review paper provides a clear discussion of various attack detection techniques, along with their benefits and drawbacks.
JAA IDS - FRAMEWORK DESIGN FOR AN EFFICIENT INTRUSION DETECTION SYSTEM
Authors:   Rajah Serfogi Government College, India, Pushpam C Amali , Jayanthi J Gnana ,   Rajah Serfogi Government College, India
Published: October 2024

Network security has become a very hot research area as its importance is heavily realized in various fields. Various mechanisms and tools are available to support this. But they do not meet the challenges imposed by fast growing technologies. Massive amounts of high dimensional data are one of the challenges. Data with a large number of features is entering and moving around the network. The Intrusion Detection System is a new mechanism that faces this challenge with the support of data mining and feature selection. In data mining, the ensemble is more preferable than a single method. In an ensemble, during the testing phase, all base classifiers are treated equally and individually participate and vote. To take a final decision, some extra effort has to be made. All these increase computation effort and time. To overcome these, this research paper proposes a new framework for intrusion detection systems using the Auto Bi-Level (ABL) classification technique with Double Filtering Fine Tuning–Ensemble Hybrid method.
ENERGY EFFICIENT BASED SECURE DATA TRANSMISSION FOR MULTI HOP TRUST MANAGEMENT TECHNIQUE USING WIRELESS SENSOR NETWORK
Authors:   Arignar Anna Government Arts College, India, S Gayathri , A Senthilkumar ,   Arignar Anna Government Arts College, India
Published: October 2024

Secure data transfer is intended to keep data safe from illegal access, damage, or disruption. In this proposed technique, an intrusion prevention system is built to counter the rapidly growing threats offered by the current generation of malware, software, and exploits. As the number of intruders has grown, the network environment has become more complicated, making threat mitigation more difficult. Modern wireless sensor networks have emerged for the aim of transmitting important information and services to an ever-growing set of users. Security is the most important issues in wireless network. Using this proposed Multi Hop Trust Management (MHTM) approach; trust management technique is used to identify the trusted nodes with malicious node. Then secured and efficient way of data transmission are directed and communicated to any kind of networks re confirmed here. The result attained shows MHTM technique attains better performance than TSRP in stipulations of energy efficiency, data transmission delay, communication overhead, throughput, malicious sensor device misclassification rate and identification.
A DEEP LEARNING TECHNIQUE FOR EFFICIENT MULTIMEDIA FOR DATA COMPRESSION
Authors:   Sathyabama Institute of Science and Technology, India, Dheepan G.M. Karpura , Rafee Shaik Mohammed ,   Sasi Institute of Technology and Engineering, India, Badugu Prasanthi ,   Jawaharlal Nehru Technological University, Kakinada, India, Kumar Sunil ,   SGT University, India
Published: October 2024

Medical image compression plays a pivotal role in efficient data storage and transmission, crucial for modern healthcare systems. This research proposes a convolutional transfer learning technique scheme tailored for multimedia data compression, specifically targeting medical images. In the background, the growing volume of medical imaging data and the demand for efficient storage and transmission underscore the need for innovative compression methods. Leveraging transfer learning from pre-trained convolutional neural networks (CNNs) designed for image recognition tasks, our methodology optimizes the compression process for medical images. The proposed scheme utilizes a pre-trained CNN’s feature extraction capabilities to capture relevant patterns in medical images, followed by fine-tuning on a specialized dataset. This approach capitalizes on the inherent ability of CNNs to learn hierarchical representations, enhancing the compression model’s adaptability to medical imaging nuances. The contribution of this research lies in the development of a tailored transfer learning scheme that effectively balances generic feature extraction and domain-specific adaptation for medical images. Results demonstrate significant improvements in compression efficiency, preserving diagnostic information while achieving substantial data reduction. The proposed scheme showcases promise for enhancing medical image storage, transmission, and retrieval systems, contributing to the advancement of healthcare technology.
EARLY MODERATE COGNITIVE IMPAIRMENT CLASSIFICATION USING ENSEMBLE OF DEEP LEARNING CLASSIFIERS
Authors:   Dr.N.G.P. Institute of Technology, A.S. Shanthi
Published: October 2024

The early detection of moderate cognitive impairment (MCI) allows for timely management, which in turn leads to improved patient outcomes. Creating a categorization system through the use of an ensemble of deep learning classifiers is the approach that this research takes in order to fulfil this goal. Using dimensionality reduction and feature selection as our primary concern, we perform preliminary processing on a diverse dataset that contains information on demographics, cognitive test scores, and brain imaging. Within the framework of an ensemble method, many deep learning architectures are utilised, with each design concentrating on a particular aspect of cognitive impairment prediction. A training and fine-tuning process is performed on each individual model on its own unique training set before the ensemble is constructed and evaluated based on a comprehensive set of performance criteria. The research presented here contributes to the development of a robust ensemble model for early MCI classification by integrating multiple different deep learning algorithms. This results in an improvement in diagnostic accuracy. The process ensures that the models may be utilised in therapeutic settings and that they are comprehensible to other individuals. When contrasted with models that are used on their own, the ensemble demonstrates superior performance, exhibiting greater memory, precision, and accuracy.
BUSINESS INTELLIGENCE BASED RECURRENT NEURAL NETWORK RNN TECHNIQUES FOR SOCIAL MEDIA IMAGE CONTENT CLASSIFICATION
Authors:   R. M. K. College of Engineering and Technology, India, P Sathyaraj , V Sudharshanam ,   R. M. K. College of Engineering and Technology, India, J Navarajan ,   Panimalar Engineering College, India, P Vijayalakshmi ,   Vels Institute of Science, Technology and Advanced Studies, India
Published: October 2024

Social media platforms like X and Facebook generate vast amounts of image content daily, necessitating automated methods for classification and analysis. Integrating Business Intelligence (BI) with Recurrent Neural Network (RNN) techniques presents a promising approach to extract valuable insights from this data. This study proposes a methodology for social media image content classification using a hybrid architecture combining Convolutional Neural Networks (CNNs) for feature extraction and RNNs for capturing temporal dependencies. The model is trained on labeled image datasets from X and Facebook, leveraging transfer learning and data augmentation techniques. The contribution lies in the fusion of BI and deep learning techniques, offering a scalable solution for real-time image content classification on social media platforms. This approach enables businesses to streamline marketing analysis, trend detection, and content moderation tasks efficiently. Experimental results demonstrate the effectiveness of the proposed methodology, achieving high accuracy in classifying diverse image content. The model''s performance is validated through comprehensive evaluation metrics, showcasing its robustness and applicability in real-world scenarios.
A DEEP LEARNING-BASED FRAMEWORK FOR ALZHEIMER'S DISEASE DIAGNOSIS AND PROGRESSION PREDICTION FROM MRI IMAGES
Authors:   Thiagarajar College of Engineering, India, M Rajalakshmi , C Karthik ,   Jyothi Engineering College, India
Published: October 2024

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by continuous cognitive and biomarker changes. Identifying predictive biomarkers for the progression from mild cognitive impairment (MCI) to AD is crucial for improving diagnostic accuracy and facilitating targeted drug development. Traditional machine learning methods have been used for AD diagnosis using MRI images, but deep learning (DL) techniques offer superior performance due to their ability to learn complex patterns from large datasets. A significant challenge in AD research is the lack of definitive biomarker signatures to predict which MCI patients will progress to AD. Current methods, such as DenseNet121, ResNet50, VGG 16, EfficientNetB7, and InceptionV3, have limitations in predictive accuracy and the ability to identify distinct neurodegenerative patterns. We developed and validated YoLoV7, a deep learning-based framework that examines neuroanatomical heterogeneity contrasted against normal brain structure to identify disease subtypes through neuroimaging signatures. Using a dataset of 1000 participants and 4000 T1-weighted MRI scans, YoLoV7 identified four patterns of neurodegeneration. We applied this framework to longitudinal data to reveal two distinct progression pathways and assessed the model's performance in predicting the pathway and rate of future neurodegeneration. YoLoV7 identified four neurodegenerative patterns, with Pattern 1 showing a 70% accuracy in predicting slower progression (Pathway A) and Pattern 2 demonstrating an 80% accuracy in predicting rapid progression (Pathway B). Overall, YoLoV7 achieved an accuracy of 85% in predicting clinical progression, outperforming traditional methods such as DenseNet121 (78%), ResNet50 (75%), VGG 16 (72%), EfficientNetB7 (80%), and InceptionV3 (76%). Measures of pattern expression offered complementary performance to amyloid/tau biomarkers in predicting clinical progression.
SEMANTIC SEGMENTATION AND CONTENT-BASED RETRIEVAL IN MULTIMEDIA IMAGE DATABASES
Authors:   Rajalakshmi Institute of Technology, India, N Kanagavalli , Hameeda Shaik ,   Avanthi Institute of Engineering and Technology, India, R Dinesh ,   Excel Engineering College, India, N Vijayaraghavan ,   Prathyusha Engineering College, India
Published: October 2024

Brain tumors, particularly gliomas, pose a significant threat to global health, necessitating accurate and efficient diagnostic methods. Magnetic Resonance Imaging (MRI) serves as a crucial tool for diagnosing glioma grades, but interpretation is subject to variability, hindering treatment planning. Intra and inter-observer variability in radiological image interpretation impede effective therapeutic strategies for brain tumor patients. Accessing relevant images from vast medical databases for comparison and treatment planning is cumbersome and time-consuming. This paper proposes a Content-Based Medical Image Retrieval (CBIR) system utilizing Convolutional Neural Network (CNN)-based feature extraction, specifically employing the AlexNet architecture. The system employs KNN clustering for indexing the feature map database and implements Gain-based feature selection to reduce feature vector dimensionality. The proposed system underwent evaluation using BraTS 2018 and 2020 datasets with five-fold cross-validation. Achieving state-of-the-art performance, the system demonstrated a mean Average Precision of 98% and Precision of 97%, showcasing its efficacy in accurately retrieving similar pathological MRI brain images.
DESIGN AND ANALYSIS ON IMAGE COMPRESSION USING NEURAL NETWORKS
Authors:   Sharnbasva University, India, Patil Shivganga , Patil Lakshmi ,   Sharnbasva University, India
Published: October 2024

Due to the explosion of video based information proliferating in the world due to the ubiquitous usage of video cameras the amount of video based information that is currently being generated around the world is huge. And due to security purposes it is becoming imperative that these video data needs to be stored in computer memory for an extended period of time for referrals by security agencies. Because of the advancement of imaging technologies that is being used nowadays it is possible to capture extremely detailed high definition images. But it is not physically possible to store all these high-definition images in computer memories for a long time because infrastructure providers will run out of memory. Image compression is a technology which assists us in this regard. Nowadays this technology has moved from image compression to video compression to compression of 3- dimensional videos which is now becoming more and more popular due to the ever increasing usage of Augmented Reality, Virtual Reality and Ec The quantity of image data produced in modern surveillance networks is rising exponentially year on year which necessitated development of novel schemes for reducing the sizes of the images captured by CCTV cameras while not compromising on the image quality increases when the images are decompressed. This paper proposed a novel Deep Neural Network based method of compressing images in which the image accuracy is not lost but the space it occupies in the memory storage of the computes is reduced greatly compared to other image compression schemes, this proposed scheme is best suited for usage in CCTVs and other networks using Internet of Things which record images and videos continuously.
The Comparative Sufficiency of ChatGPT, Google Bard, and Bing AI in Answering Diagnosis, Treatment, and Prognosis Questions About Common Dermatological Diagnoses
Authors: Chau Courtney A , Feng Hao , Cobos Gabriela , Park Joyce
Published: October 2024

Our team explored the utility of unpaid versions of 3 artificial intelligence chatbots in offering patient-facing responses to questions about 5 common dermatological diagnoses, and highlighted the strengths and limitations of different artificial intelligence chatbots, while demonstrating how chatbots presented the most potential in tandem with dermatologists’ diagnosis.
RESPONSE OF EXOTIC VEGETABLES TO PLANT SPACING, FERTILIZATION, AND INSECT PEST MANAGEMENT UNDER SUBTROPICAL REGION OF WESTERN HIMALAYAS OF HIMACHAL PRADESH
Authors: RENU KAPOOR  , DHARMINDER KUMAR  , CHANDEL VGS  , ANIL KUMAR  , ANURAG SHARMA  , RAJESH KALER  , VIPAN GULERIA
Published: October 2024

Objective: The study was conducted with the objective to study the effect of different plant spacing and fertilizer treatments on the yield and growth parameters of exotic vegetables.
Methods: The study was carried out at the experimental farm of Regional Horticultural Research and Training Station Jachh (32° 16’ N latitude and 75°51’ E longitude, 440 m above mean sea level) during the year 2021–22. The experiment was laid out in a factorial randomized complete block design with three replications. The treatments were comprised of three levels of fertilizers (120, 100, 75% RDF) and two levels of spacing.
Results: The observations revealed significant variations in the fresh yield of Kale, Parsley, and Celery in response to different fertilizer doses and plant spacing. Specifically, the fresh yield of Kale, Parsley, and Celery showed significant improvements with higher levels of fertilization (F1) and narrow plant spacing (S2). In addition, the number of leaves per plant increased with higher fertilizer doses but did not vary significantly across different spacing levels in all three crops. While the leaf length and width of Kale were not significant, the leaf length of Parsley and Celery varied significantly with increasing fertilizer doses. Furthermore, among the three crops studied, Kale was observed to be infested by aphids, particularly Brevicoryne brassicae, during the crop period. The application of imidacloprid (0.5 ml/L) and chlorantraniliprole (0.4 ml/L) demonstrated effective management of this pest, providing better results for pest control.
Conclusion: Fresh yield of kale, parsley, and celery increased under high level of fertilization and narrow spacing as well. Moreover, imidacloprid and chlorantraniliprole can be utilized for the management of cabbage aphids in Kale crop.
CRITICAL ANALYSIS OF INDIAN MONSOON RAINFALL FORECASTING
Authors: ANIL KUMAR S
Published: October 2024

Extensive study has been undertaken in recent years to comprehend climate changes and their potential impacts, aiming to mitigate natural disasters such as droughts, snowfall, floods, and more. This paper provides a comprehensive literature review focusing on the variability in rains during the Indian monsoon, employing statistical analysis, modeling, and forecasting techniques applied by various researchers over the years. The findings of this study reveal that the variability in Indian rainfall time series can be attributed to a range of tools and methodologies. This variability plays a key part in the development and prediction of future rainfall forecasting models. Notably, Data on historical rainfall time series have predominantly centered on broad regional scales in India and its subdivisions. To better serve the needs of farmers and bolster the Indian economy, there is an urgent need to develop new statistical models at smaller spatial and temporal scales. Throughout this review, it becomes clear that there exists an average monsoon rainfall level in India, which functions as a benchmark. Rainfall below this threshold is considered a poor monsoon, while rainfall exceeding it is regarded as a good monsoon. It is worth noting that a substantial 70% of the annual rainfall occurs in India during the monsoon season, encompassing the period from June to September. The average monsoon rainfall in India, calculated based on data spanning a century from 1901 to 2000, stands at 85 cm. Notably, specific years such as 2002, 2004, 2009, 2014, 2015, and 2016 have witnessed below-average rainfall. This comprehensive review serves as a wonderful resource for readers interested in learning more about the intricacies of monsoon rainfall variability in India and its potential modeling through appropriate methodologies.
ANAPHYLACTIC DEATH CAUSED BY LIVER HYDATID CYST RELYING ON THE IMPORTANCE OF AUTOPSY – A CASE REPORT
Authors: Sadeghi Sajjad
Published: October 2024

Hydatid cyst is a common disease between humans and dogs as the main host and sheep as the intermediate host with the parasitic agent Echinococcus. There have been reports of hydatid cyst rupture spontaneously or following trauma. Autopsy, as the best way to determine the cause of death, can be the witness of the occurrence of a crime or an event. In the current study, a child death, that occurred as a result of a collision with another child at school, was investigated after his father complained to the school principal, and an autopsy was performed by forensic medicine. In forensic reports, the cause of death was anaphylactic shock due to a cyst in the right lobe of the liver. According to the pathology report, the cause of the liver cyst was a parasitic infection of Echinococcus granulosus.
ASSESSMENT OF TEACHING AND LEARNING ENVIRONMENTS AMONG SECONDARY SCHOOLS IN MKPAT ENIN LOCAL GOVERNMENT AREA OF AKWA IBOM STATE, NIGERIA
Authors: ALPHONSUS PATRICK UDOH  , FAITH EJIROGHENE UDOH
Published: October 2024

This paper defines teaching and learning environments to be diverse physical locations, contexts, and cultures in which students learn. The paper also describes Secondary education as that education that follows immediately after primary education and is also referred to as post primary education. The purpose of the study is to assess teaching and learning environment among secondary schools in Mkpat Enin Local Government Area of Akwa Ibom State specifically the extent to which ICT has been integrated for teaching and learning environment among secondary schools in Mkpat Enin Local Government Area Descriptive research design was used in the study. The population comprises 14 secondary schools in the study area with all the teachers and principals as sample. The instrument used for the collection of data was tagged Teaching and Learning Environment (TALEO) questionnaire. The modify Likert type rating scale of strongly Agree (4) Agree (3) Disagree (2) Strongly Disagree (1) was apply. Findings among other were that teaching and learning environment among secondary schools in Mkpat Enin Local Government Area are not conducive. The paper concluded that school administrators with the stakeholders should create a conducive environment for teaching and learning in the schools in order to enhance better teaching-learning activities.
SPEAKING CLASS EXERCISES USING THE SHADOWING PODCAST CONTENT TO WORK ON PRONUNCIATION
Authors: Suseno Edy
Published: October 2024

English as a foreign language 1st-year students is introduced to speaking flaws. To remedy the pronunciation faults, 10 freshmen joined the treatment. To take part, they need access to gadgets with dependable internet. As a way to conduct the lesson outside of the classroom, the instructor set up a WhatsApp group. After finishing with the student member list, the teacher posted the podcast’s monologue’s content. He instructed the student to push the play button to hear the first sentence. Then, they paused and stated it once more. They went on until the delivery was finished. To perform shadowing, the student repeated the voice from the beginning. They all simultaneously spoke in response to the voice they heard. Continue doing this till the speech is over. After that, the transcript was spoken aloud for recording and forum posting. The lecturer then displayed the URL for the podcast’s talk. He repeated to them the same instructions; they had been given before. The students’ uploaded recording was entered into the transcription application to get the transcription. The teacher evaluated the transcription generated by a native speaker with the findings to find out how many pronunciation problems there were. All of the data were analyzed using a mixed-methods approach. The results showed that once the treatment started, the pupils’ pronunciation improved. Teachers can adapt this discovery to teach the related disciplines. It also inspires other researchers to conduct more research.
USING PODCASTS TO IMPROVE LANGUAGE PROFICIENCY FOR ENGLISH AS A FOREIGN LANGUAGE STUDENTS
Authors: Suseno Edy
Published: October 2024

To communicate their ideas to one another, students use language. They require linguistic proficiency to improve their fluency. Still, a lot of English as a foreign language students struggled to communicate their intentions to other language users. They require a way to facilitate the development of their language proficiency. The application that offers real content is podcasts. By employing them, the pupils can improve their linguistic abilities. The content includes various dialects, speech patterns, and languages. Learning about those resources helps students become more natural and fluid in their speaking. Furthermore, the resources offered in the areas of news, narrative, self-improvement, linguistic variety, voice, accents, and speaking styles facilitate the students in improving their language proficiency. Students’ vocabulary, problem-solving techniques, capacity for introspection, and language proficiency all increase as a result. Through a literature review of the textual content to improve language acquisition, it is determined that using podcast materials helps students improve their speaking, reading, writing, and listening skills. Teachers can use this type of research to their advantage by applying it to the teaching of related disciplines. It also encourages further investigation into similar subjects by other scholars
Study on the City Image of Maoming Based on Drone Photography Data
Authors: Peng Jiaqiang , Wang Ruei-Yuan , Huang Rongrong , Liu Juncheng , Yang Pengsheng
Published: October 2024

This study is based on drone photographs and related data of Maoming City obtained from the social platform "Sky City." Through methods such as computer vision image analysis, kernel density analysis (KDA), and standard deviational ellipse analysis, the research explores the spatiotemporal distribution characteristics of drone-captured imagery, the visual features of the city image, and the clustering of city image elements. By analyzing the city image of Maoming from a “God’s-eye view,” the study offers insights into the construction of Maoming's city image and drone management, providing recommendations for enhancing urban design and improving the quality of urban space.
ICT and Language Learning Interconnections: A Review of the Literature
Authors: Lakhal Mounim , Hakimi Mohammed
Published: October 2024

The effects of information and communication technologies (ICT) in the field of education have long been discussed and argued by many educational scholars worldwide. Today’s youths are surrounded by information technologies. They use and interact with digital media on a constant basis, to the point where it becomes part of their daily lives. The current review provides clear evidence about the complex nature of social media and its relationship with language teaching and learning¬—an issue that has perplexed numerous scholars and led to controversial views. While some scholars blame social media for being a source of distraction, others view it as a valuable educational tool, and others take a balanced approach, highlighting its pros and cons. Given that social media utilization is a behavior, this study provides a critical synthesis of theoretical and empirical research and highlights several relevant theories, on top of which comes the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which offers a theoretical framework for probing students’ perceptions and attitudes towards technology adoption. The studies reviewed in this paper highlight learners’ perceptions of and attitudes towards the use of ICT in the learning process, and the present review aims to provide valuable insights for education stakeholders.