Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network

Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion. This research presents an explainable framework for detecting spam images using Convolutional Neural Network(CNN) algorithms and Explainable Artificial Intelligence (XAI) algorithms. In this work, we use CNN model to classify image spam respectively whereas the post-hoc XAI methods including Local Interpretable Model Agnostic Explanation (LIME) and Shapley Additive Explanations (SHAP) were deployed to provide explanations for the decisions that the black-box CNN models made about spam image detection. We train and then evaluate the performance of the proposed approach on a 6636 image dataset including spam images and normal images collected from three different publicly available email corpora. The experimental results show that the proposed framework achieved satisfactory detection results in terms of different performance metrics whereas the model-independent XAI algorithms could provide explanations for the decisions of different models which could be utilized for comparison for the future study.Comment: Under review by International Conference on Cyber Resilience (ICCR), Dubai 202

Mobilization of Cyber Extension Participants to Build Household Food Security

Youtuber, Tiktokers and content creater exist on internet who actively showing agricultural information in limited land, hydroponic cultivation also tips for utilization home yard as plantation productive a form public participation in an attempt to increase agricultural in micro scale. The impressions agricultural innovative and creatives from content creators in internet is a phenomenon change occurs paradigm education present that openly also no need to rely on employment of counseling functional with status State Civil Apparatus. Absence content creator information about alternative agricultural can categorize to participant cyber extension in order to build food resilience at the household level. Cyber extension that is conducted on public became concept participative counceling that are relevant developed when limited quantity human resources agricultural counselors, procurement material and support facilities. Purpose: studies on existence participant cyber extension to design programs and strategy mobilize content creator into intensify extension utilization home yard to plant productive to build food resilience household. This study improving concept couceling based society and mobilization power source theory. Method: systematics literatur review with qualitative approach. Result: (1) Need to be given coaching or mentoring for participants especially content creator to produce mater that are educative and solutive for society who want start plating at home yard (2) Giving appreciation or reward to participant who active and creatuve doing cyber extensionKeberadaan YouTuber, Tiktokers dan Content Creator di internet yang giat menayangkan informasi  pertanian di lahan terbatas,  budidaya tanaman hidroponik serta kiat memanfaatkan pekarang rumah sebagai lahan perkebunan produktif merupakan sebuah wujud partisipasi masyarakat dalam  upaya meningkatkan produksi pertanian skala mikro. Tayangan pertanian inovatif dan kreatif dari para pembuat konten di internet adalah fenomena  terjadinya perubahan paradigma penyuluhan masa kini yang terbuka serta tidak harus mengandalkan kerja penyuluh  fungsional dengan status Aparatur Sipil Negara (ASN). Kehadiran pencipta konten (content creator) informasi pertanian alternatif dapat dikategorikan sebagai partisipan penyuluhan di ruang siber atau cyber extension dalam rangka membangun ketahanan pangan pada tingkat rumah tangga. Cyber extension yang dilakukan publik menjadi konsep penyuluhan partisipatif yang relevan dikembangkan ketika terbatasnya jumlah Sumber Daya Manusia (SDM) penyuluh pertanian, pengadaan materi dan fasilitas pendukungnya. Tujuan:  kajian mengenai eksistensi partisipan cyber extension untuk merancang program maupun strategi memobilisasi pencipta konten dalam menggencarkan penyuluhan pemanfaatan pekarangan untuk tanaman produktif guna membangun ketahanan pangan rumah tangga. Kajian ini mengembangkan konsep penyuluhan berbasis masyarakat dan teori mobilisasi sumber daya. Metode: sistematics literatur review dengan pendekatan kualitatif. Hasil: (1) Perlu diberikan pembinaan atau pendampingan bagi partisipan utamanya pencipta konten untuk menghasilkan materi yang bersifat edukatif dan solutif bagi masyarakat yang ingin memulai menanam di pekarangan. (2) Memberikan apresiasi atau reward kepada partisipan yang aktif dan kreatif melakukan cyber extension

Handwritten Arabic Character Recognition for Children Writ-ing Using Convolutional Neural Network and Stroke Identification

Automatic Arabic handwritten recognition is one of the recently studied problems in the field of Machine Learning. Unlike Latin languages, Arabic is a Semitic language that forms a harder challenge, especially with variability of patterns caused by factors such as writer age. Most of the studies focused on adults, with only one recent study on children. Moreover, much of the recent Machine Learning methods focused on using Convolutional Neural Networks, a powerful class of neural networks that can extract complex features from images. In this paper we propose a convolutional neural network (CNN) model that recognizes children handwriting with an accuracy of 91% on the Hijja dataset, a recent dataset built by collecting images of the Arabic characters written by children, and 97% on Arabic Handwritten Character Dataset. The results showed a good improvement over the proposed model from the Hijja dataset authors, yet it reveals a bigger challenge to solve for children Arabic handwritten character recognition. Moreover, we proposed a new approach using multi models instead of single model based on the number of strokes in a character, and merged Hijja with AHCD which reached an averaged prediction accuracy of 96%.Comment: 1

Analysis of postures for handwriting on touch screens without using tools

The act of handwriting affected the evolutionary development of humans and still impacts the motor cognition of individuals. However, the ubiquitous use of digital technologies has drastically decreased the number of times we really need to pick a pen up and write on paper. Nonetheless, the positive cognitive impact of handwriting is widely recognized, and a possible way to merge the benefits of handwriting and digital writing is to use suitable tools to write over touchscreens or graphics tablets. In this manuscript, we focus on the possibility of using the hand itself as a writing tool. A novel hand posture named FingerPen is introduced, and can be seen as a grasp performed by the hand on the index finger. A comparison with the most common posture that people tend to assume (i.e. index finger-only exploitation) is carried out by means of a biomechanical model. A conducted user study shows that the FingerPen is appreciated by users and leads to accurate writing traits

Handwritten OCR for Indic Scripts: A Comprehensive Overview of Machine Learning and Deep Learning Techniques

The potential uses of cursive optical character recognition, commonly known as OCR, in a number of industries, particularly document digitization, archiving, even language preservation, have attracted a lot of interest lately. In the framework of optical character recognition (OCR), the goal of this research is to provide a thorough understanding of both cutting-edge methods and the unique difficulties presented by Indic scripts. A thorough literature search was conducted in order to conduct this study, during which time relevant publications, conference proceedings, and scientific files were looked for up to the year 2023. As a consequence of the inclusion criteria that were developed to concentrate on studies only addressing Handwritten OCR on Indic scripts, 53 research publications were chosen as the process's outcome. The review provides a thorough analysis of the methodology and approaches employed in the chosen study. Deep neural networks, conventional feature-based methods, machine learning techniques, and hybrid systems have all been investigated as viable answers to the problem of effectively deciphering Indian scripts, because they are famously challenging to write. To operate, these systems require pre-processing techniques, segmentation schemes, and language models. The outcomes of this methodical examination demonstrate that despite the fact that Hand Scanning for Indic script has advanced significantly, room still exists for advancement. Future research could focus on developing trustworthy models that can handle a range of writing styles and enhance accuracy using less-studied Indic scripts. This profession may advance with the creation of collected datasets and defined standards

A Study of Techniques and Challenges in Text Recognition Systems

The core system for Natural Language Processing (NLP) and digitalization is Text Recognition. These systems are critical in bridging the gaps in digitization produced by non-editable documents, as well as contributing to finance, health care, machine translation, digital libraries, and a variety of other fields. In addition, as a result of the pandemic, the amount of digital information in the education sector has increased, necessitating the deployment of text recognition systems to deal with it. Text Recognition systems worked on three different categories of text: (a) Machine Printed, (b) Offline Handwritten, and (c) Online Handwritten Texts. The major goal of this research is to examine the process of typewritten text recognition systems. The availability of historical documents and other traditional materials in many types of texts is another major challenge for convergence. Despite the fact that this research examines a variety of languages, the Gurmukhi language receives the most focus. This paper shows an analysis of all prior text recognition algorithms for the Gurmukhi language. In addition, work on degraded texts in various languages is evaluated based on accuracy and F-measure

Sequence-to-Sequence Contrastive Learning for Text Recognition

We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence structure, each feature map is divided into different instances over which the contrastive loss is computed. This operation enables us to contrast in a sub-word level, where from each image we extract several positive pairs and multiple negative examples. To yield effective visual representations for text recognition, we further suggest novel augmentation heuristics, different encoder architectures and custom projection heads. Experiments on handwritten text and on scene text show that when a text decoder is trained on the learned representations, our method outperforms non-sequential contrastive methods. In addition, when the amount of supervision is reduced, SeqCLR significantly improves performance compared with supervised training, and when fine-tuned with 100% of the labels, our method achieves state-of-the-art results on standard handwritten text recognition benchmarks