Journal of Information Communication Technologies and Robotic Applications
Journal of Information Communication Technologies and Robotic Applications (JICTRA) is open access peer-reviewed journal that provides a forum to the academic and industrial community to report the latest innovation and identify emerging trends in the field of telecommunication technologies, machine learning and Artificial intelligence, robotic systems, wireless networks, and mobile computing. Special emphasis is given to technology innovation and real-world applications. The main objective of Robotics is to establish an excellent channel of communication between experts in academic and research institutions, practitioners and professionals working in the industry, and policymakers. Studies may relate, but are not limited, to the following categories: Wireless sensor networks for robot navigation, Artificial Neural Networks, Database Management System, Software Engineering and Architecture, Software Project Management, Dynamic Programming, Digital Signal Processing Theory, Distributed Sensor Networks, Wired/wireless Sensor, Wireless technologies which includes 3G And 4G Mobile communications services, ad-hoc and sensor networks, Genetic Engineering and Technologies, Satellite Engineering and Technology, Mathematical logic and Modeling, Embedded System on Chip. RF circuits Design, Image Processing, Analysis and Recognition, Genetic and Evolutionary Algorithms, Mathematical/analytical modeling, and computer simulation.
Newports Institute of Communications and Economics (NICE), Karachi Pakistan
PC Algorithm using MATLAB-Based Time-Domain Signal Processing forUWB Wall Gap Detectio
In this work, a Peak Clustering (PC) algorithm and MATLAB based time-domain signal processing of Ultra-Wideband (UWB) wall gap detection is presented. The primary aim of this work is to develop an improved computational algorithm that can process received UWB signal data and find those peaks that are significant to allow separation distance between wall structures to be estimated. The approach under consideration in this paper is based on the study of received UWB signals to determine the distance of gaps within the wall structure. The received signals are obtained by simulation with a CST Studio Suite simulation model using a UWB antenna for transmitting and receiving. Then the received signal is imported into MATLAB for processing. The proposed algorithm is comprised of noise removal, peak detection and peak clustering to extract reflection patterns of different interfaces in the wall. The time delay between clustered reflections is calculated and applied to calculate the distance between the gap using the time-of-flight principle. The simulation results show that the proposed PC algorithm is effective and can accurately detect the wall gaps ranging from 300 mm to 500 mm. It shows how to properly perform signal processingbased gap detection suitable for security, search and rescue and structural inspection applications. Keywords: Peak Clustering Algorithm, Time-Domain Signal Processing, MATLAB, Peak Detection, Wall Gap Detection, Ultra-Wideband (UWB)
Copyright (c) 2026 Sajjad Ahmed, Farhan Bashir, Imran Khan Keerio, Amna Shaikh, Muhammad Juman Jhatial https://creativecommons.org/licenses/by-nc/4.0
Leveraging Natural Language Processing for Predictive Modeling
Weather analysis is the process of examining parameters like atmospheric pressure, humidity, and wind speed, to make calculations and predictions regarding the current weather. Using the publicly available dataset made available by CrowdFlower on Kaggle, this research performs operations of natural language processing and predictive modeling on the provided data to determine the sentiment of a tweet, and predict the type of weather being mentioned in the tweet. We first perform the data-pre-processing operations of label engineering, feature extraction, and data visualization on the available data. Regression approaches including Gradient Boosting, Ridge, and Linear regressor, and classification approaches including Random Forest, Multinomial Naïve Bayes, Linear Support Vector, and Stochastic Gradient Descent classifier were then applied for data modeling. The results of the current research indicate that feature reduction has a positive effect on overall predictive accuracy. The results also indicate that predictive performance is higher when performing classification tasks as compared to regression tasks.
Copyright (c) 2026 Areej fatemah Meghji, Veena Kumari, Farhan Bashir https://creativecommons.org/licenses/by-nc/4.0
An Adaptive Sensor Data Access Framework for Mobile and Web Environments
Wireless Sensor Networks (WSNs) have emerged as a transformative technology, facilitating the real-time collection and dissemination of data from a myriad of physical environments. From environmental monitoring and healthcare to industrial automation and smart cities, the applications of WSNs are diverse and impactful. However, the increasing complexity and dynamic nature of these applications pose significant challenges to efficient and adaptive data access in WSNs. The proposed research addresses the multifaceted landscape of WSNs, acknowledging their crucial role in diverse domains. These networks consist of spatially distributed sensors that collaboratively collect data and communicate it to a central processing unit. Applications of WSNs range from monitoring environmental parameters such as temperature and humidity to more complex tasks like tracking healthcare metrics and managing smart infrastructure. Despite their versatility, WSNs face several challenges that hinder their optimal performance. These challenges include energy constraints of sensor nodes, the dynamic nature of the environment, and the need for realtime and reliable data access. In particular, the adaptability of WSNs to changing conditions and user requirements remains a critical research challenge. The focus of this research is the development of an Adaptive Sensor Data Access Framework that addresses the challenges. This framework is envisioned to seamlessly integrate with mobile and web environments, thereby providing a responsive and adaptive approach to accessing sensor data in real time. By prioritizing adaptability, the research aims to enhance the overall efficiency, reliability, and user-centric customization of sensor data access. The Adaptive Sensor Data Access Framework will encompass advanced algorithms and mechanisms for real-time adjustments to changing environmental conditions, varying network dynamics, and evolving user preferences. The integration with mobile and web applications will be designed to be seamless, ensuring that the end-users experience a continuous and responsive flow of sensor data tailored to their specific needs. This research is positioned at the intersection of WSNs, mobile/web applications, and adaptive computing. The outcome is expected to yield a groundbreaking framework that not only addresses the challenges prevalent in WSNs but also sets the stage for a new paradigm of user-centric, adaptive sensor data access. The significance of this research extends to applications in environmental monitoring, healthcare, industrial automation, and beyond, where timely and accurate data access is paramount. The proposed research, through the development and validation of the Adaptive Sensor Data Access Framework, aims to contribute significantly to the evolution of WSNs, making them more resilient, user-friendly, and adaptable to the ever-changing demands of contemporary applications
Copyright (c) 2026 Ariz Muhammad Brohi, Yasir Arfat Malkani, Abdul Waheed Mahesar, Muhammad Tahir https://creativecommons.org/licenses/by-nc/4.0
Predicting Suicidality Indicators from Depression Symptom Checklists Using Interpretable Machine Learning
Early identification of individuals at elevated risk of suicidality is a critical component of mental health prevention and intervention. In many applied settings, assessment relies on self-reported depression symptom checklists rather than comprehensive clinical evaluations. This study investigates whether suicidality indicators can be predicted using only non-suicidality depression symptoms, while emphasizing interpretability and screening-oriented evaluation. Using a publicly available depression checklist dataset comprising 2,523 unique responses to 25 Likert-scale items, suicidality outcomes were constructed from three suicidality-related items and excluded from the predictor set to prevent information leakage. Interpretable machine learning models, including logistic regression and shallow decision trees, were compared with a gradient boosting model under stratified five-fold cross-validation. Model performance was evaluated using discrimination metrics, threshold-dependent screening measures, and probability calibration. The best- performing model achieved an AUROC of approximately 0.78, while interpretable baselines performed competitively. High-recall operating thresholds substantially reduced false negatives, highlighting the suitability of the approach for screening applications. Calibration analysis showed that isotonic calibration improved the reliability of predicted probabilities. Symptoms reflecting negative self-concept, hopelessness, and anhedonia emerged as the strongest predictors of suicidality indicators. Although outcomes were proxy measures derived from self-report data rather than clinical diagnoses, the results demonstrate that interpretable machine learning can extract clinically plausible screening signals from standard depression checklists. This work provides a transparent and reproducible framework for ethically grounded suicidality risk screening research.
Copyright (c) 2025 Farhan Bashir, Marvi, Roshan Kumar, Shakeel Leghari, Areej Fatima, Shuaib Ahmed Wadho https://creativecommons.org/licenses/by-nc/4.0
An efficient Semantic Search System to Retrieve Photos using CLIP, BLIP and FAISS
The rapid growth of personal photo collections has highlighted the limitations of traditional retrieval methods that rely on filenames or manual tagging, creating a persistent semantic gap between image content and user intent. This study proposes an efficient semantic search system that enables natural-language-based retrieval of personal photos through a hybrid multimodal architecture. The system integrates CLIP for vision–language alignment, BLIP for automatic caption generation, and FAISS for high-speed similarity search, combining direct visual semantic matching with text-mediated understanding. Evaluated on a dataset of 2,000 personal images, the system demonstrates strong retrieval performance, achieving mean cosine similarity scores of 0.254-0.282 and perfect Precision@6 (1.000) for attribute based queries. Results indicate high robustness to variations in lighting, pose, and background, with the strongest performance observed in clothing- and object-related searches, while event-based queries remain more challenging. Key contributions include a privacy-preserving on-device design, a dual-pathway multimodal workflow, and a comprehensive evaluation framework for semantic photo retrieval. Overall, the proposed system effectively bridges the semantic gap in personal photo management and demonstrates the practical value of multimodal AI for intuitive, human centred image retrieval.
Copyright (c) 2025 Imtiaz Ali Dahri, Fida Hussain Dahri, Nisar Ahmed Dahri, Sajjad Hussain Bhutto https://creativecommons.org/licenses/by-nc/4.0
A Comprehensive Review of Complex Network Methods for Cotton Plant Disease Detection
In recent years, researchers have become increasingly interested in cotton, a precious cash crop everywhere from Pakistan to China to Texas, is crucial for the textile industry and agricultural economies around the world. But its production is continuously threatened by a diversity of plant diseases, which can be triggered by pathogens such as bacteria, fungi and viruses. The conventional diagnostic approaches to confirm these diseases are generally not sensitive, time-consuming, and at the same time laborious. In recent times, network biology approaches have emerged as a robust platform in predicting disease associations and simulating diseases interplay in plants. The purpose of this paper is to investigate the application of complex network theory in cotton plant disease detection and control, particularly for the integration with machine learning and artificial intelligence. However, as many techniques are promising, only a few real-time interpretable field deployable models exist up to now to be used in local agricultural systems, especially in developing countries such as Pakistan. From this perspective, I propose that lightweight hybrid models should be developed which integrating network theory with spatiotemporal environment data, and open-access database would get higher availability. I believe there is a big opportunity in having a mobile-based decision support system, which is based on the graph learning model and can be used by farmers. This survey recommends that although substantial contributions have been achieved, there are still research opportunities for integrating complex networks into realistic and scalable systems regarding plant disease detection. This paper aims to explore the application of complex network theory in cotton plant disease detection and management, focusing on integrating with machine learning and artificial intelligence. However, despite many promising methods, there is still a lack of real-time, interpretable, and field-deployable models that can be used in local agricultural systems, particularly in developing countries like Pakistan. Based on this analysis, I recommend the development of lightweight hybrid models that combine network theory with spatiotemporal environmental data, and the use of open-access datasets to enable broader adoption. I also see great potential in integrating mobile-based decision support systems powered by graph learning models for use by farmers directly. This review concludes that while significant progress has been made, the integration of complex networks into practical, scalable solutions for plant disease detection remains an open and valuable research direction.
Machine Learning and Artificial Intelligence
Copyright (c) 2025 Hina Shafi, Ali Ghulam, Sajjad Hussain Talpur, Rahu Sikander, Aamir Ali https://creativecommons.org/licenses/by-nc/4.0
Hybrid Deep Learning Models for Enhanced Lung Cancer Detection from Medical Imaging
Lung cancer is one of the most frequent causes of cancer related mortality in the world and early diagnosis is very essential in improving survival rates of the patients; nevertheless, traditional imaging technologies are usually characterized by poor results at differentiating benign and malign lesions, particularly in problematic cases such as small or ground-glass nodules, which leads to unnecessary high rates of false positive and false negative. To cope with them, this work proposes a new hybrid deep learning framework that combines convolutional neural networks (CNNs), long short-term memory (LSTM) recurring units, and transformer-based self-attention mechanisms to utilize both local spatial textures, temporal-sequential features, and contextual information of CT images. The multi-task learning implicit framework allows addressing issues of segmentation, detection, and classification in a single framework, enhancing both speed and quality. On a large, strictly annotated dataset of 1,190 CT slices of 110 patients, the hybrid model secured the highest accuracy at 95 percent, significantly outperforming the best baselines, including CNN (90.5 percent), Transformer (91.2 percent), and LSTM-CNN (92.7 percent), as well as its precision and recall both to the tune of 95 percent. The gains in the model performance are supported by the fact that the F1 score is improved, and additional evidence of the model improvements is visualization tools such as attention maps and feature importance scores, which strengthen model interpretability and clinical trust. These findings highlight the clinical opportunity of the hybrid method to enhance lung cancer diagnostic performance through increasing the accuracy of early detection, minimizing unnecessary invasive procedures, and providing personalized treatment plans. This paper presents the need to further substantiate in different multi-institutional data sets and stresses the future efforts on computational optimization and interpretability improvement, to convert the promising hybrid model into a scalable, clinically deployable decision support system to manage lung cancer.
Copyright (c) 2025 Muhammad Hamza Khurram, Naeem Aslam, Hira Saleem https://creativecommons.org/licenses/by-nc/4.0
Smart Class Analytics for Assessing Group Understandingvia Facial Expressions
Understanding the comprehension level of students in a communicating classroom is critical. As technology becomes increasingly integral to our lives, advanced facial emotion recognition becomes a powerful tool for assessing comprehension levels in a real-time scenario. We created a system to measure cognizance levels and analyze facial emotions to ascertain comprehension. In this research, we presented a new system for assessing the comprehension level of a group setting through facial expressions in a real-time. Our techniques, supported by advanced DL techniques like OpenCV and Deep Face provide us with different backend options that are very effective for facial emotion recognition and analysis such as MTCNN and Retina face. Initially, we used OpenCV for real-time capturing video by computer webcam and capture the frames at regular intervals. The frames are stored in a pre-defined directory. After that, we defined a function that iterates over each frame for detecting emotions with facial landmarks and also shows the dominant emotion with each face in the frame. A pretrained MTCNN model (one of the state-of-the-art models provided by Deep Face) has been utilized for detecting the face present in each frame and facial landmark localization. MTCNN is a lightweight model that detects faces with high accuracy and speed. Finally, The Deep Face analyze () function has been utilized for emotion analysis in the wild with the MTCNN backend which provides insights into the dominant emotion related to each face. Comprehension levels were measured as per emotional states detected, with positive affect [‘happy’, ‘neutral’ (when confidence is >70), and ‘surprised’ (when confidence is <= 60%)] implying higher comprehension, negative affect [‘sad’, ‘disgust’, ‘fear’, and ‘surprised when the confidence level is above 60%’] hinting at lower comprehension, and when [‘happy’, ‘neutral’] emotions having confidence <=70% hinting to moderate affect. The measured comprehension level is then disseminated into high, moderate, and low. The finding exposed the complex interrelation among facial expressions, emotions, and comprehension levels in intragroup interactions. This research supports the development of understanding human behavior and interaction dynamics, with connection to domains such as psychology, human-computer interaction, and education.
Copyright (c) 2025 Haider Ali, Muhammad Adnan Kaim Khani, Abdul Salam Shah, Asif Ali Laghari, Muhammad Minam https://creativecommons.org/licenses/by-nc/4.0
Mitigating Bias in AI Systems: A Comprehensive Review of Sources and Strategies
Artificial intelligence (AI) is increasingly deployed in high-stakes environments such as healthcare, recruitment, finance and public safety. Although AI promises efficiency and accuracy, substantial evidence shows that it can reproduce or amplify societal inequalities through biased datasets, algorithmic design choices, human cognitive bias and automation bias. This study conducts a systematic literature review of 128 peer-reviewed publications from 2015 to 2025 to examine the sources of bias in AI systems and evaluate the effectiveness of mitigation strategies. Bias is shown to originate at multiple stages of the AI lifecycle, data, model development and real-world deployment, and tends to re-emerge when mitigation methods are applied in isolation. In response, this study introduces the Integrated Bias-Mitigation Framework (IBMF), a lifecycle-oriented model that aligns technical interventions with governance and continuous monitoring. The findings highlight the need for coordinated mitigation strategies rather than one-off technical solutions. The study concludes with policy, organizational and research recommendations to support sustainable fairness and accountability in AI.
Artificial Intelligence; Algorithmic Bias; Fairness; Bias Mitigation; Responsible AI; Automation Bias; Cognitive Bias
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
AI-Augmented Project Management for Digital Transformation in Insurance Firms
Digital transformation in the insurance industry increasingly relies on artificial intelligence (AI) to enhance project management practices, enabling firms to execute complex technology-driven initiatives with greater precision and efficiency. This study examines how AI-augmented project management transforms traditional approaches often constrained by manual decision-making, fragmented oversight, and limited predictive capability into intelligent, data-driven processes. Drawing on a synthesis of recent industry reports, secondary benchmarks, and an illustrative case from the insurance sector, the study analyses how AI tools such as predictive analytics, machine learning, and intelligent automation can be embedded across the project lifecycle. The results show that AI-augmented project management is associated with substantial performance gains: forecast accuracy, on-time delivery, risk detection, resource utilization, and stakeholder satisfaction all improve by 40–55% compared with traditional practices. These improvements translate into fewer delays and budget overruns, stronger alignment between IT initiatives and business strategy, and higher return on investment (ROI) for digital transformation projects. The findings support the notion of augmented intelligence, where human expertise is amplified rather than replaced, enabling continuous learning, adaptive governance, and more resilient project execution. The study concludes by proposing an AI-augmented project management framework for insurance firms and outlining implications for practitioners and future research.
AI-augmented project management, digital transformation, insurance industry, predictive analytics, intelligent automation, augmented intelligence, risk mitigation, resource optimization
Measuring Global Internet Speed by Using Complex Network
Today is the 21st advent the era of modern digital world, the digital world where internet and speed of internet is the main concern for individuals and organizations by using computer networks. Therefore, in this research we formalize and analyze the average usage of worldwide countries by using complex network and model the dataset of worldwide internet speed. Furthermore, using this data set we observe the complex network with global internet speed by average (Degree Distribution, Betweenness, and Closeness) by using weighted directed graph while the edge merge strategy is sum.
Worldwide, internet, degree, edge, Betweenness, closeness global, average and sum
Mitigating Phishing and Social Engineering Ransomware Threats in Bioinformatics: Strategies for Education and Awareness
Phishing and social engineering attacks, along with ransomware, have become an unforeseen obstacle in the quickly developing field of bioinformatics, putting confidential information at risk and interfering with research procedures. As the bioinformatics landscape continues to change, addressing cybersecurity vulnerabilities becomes a critical concern. Similarly, even the most knowledgeable experts in the given area may fall prey to such cyber dangers. Social engineering and phishing are the types of attacks that exploit people's weaknesses, thus a comprehensive approach by integrating technical fixes, awareness and education campaigns aimed at fighting cyber threats should be embraced. In this case, bioinformatics experts can traverse the digital sphere with resolve and get rid of bad data and maintain the integrity of the domain of science as they grow a responsible community. As a result of the systematic review we conducted, from the 551 studies we found, 20 that met the eligibility criteria were chosen for further analysis. This analysis of literature concentrated on the defence’s biotechnology against ransoming crooks` constantly morphing strategy. The report went over a couple of things, like the increased complexness of encryption algorithms, and the RaaS (ransomware-as-a-service) model. This study offers a new toolkit focused on campaigns to sensitize the public and educate them to address the threats and dangers presented by ransomware. The physicists of bioinformatics are equipped to withstand the digital world with staunchness and see to securing the non-negotiables essential data safeguard and the limited but fundamental research activities by developing an elaborate culture of cybersecurity awareness.
Lightweight Machine Learning Models for Real-Time Ransomware Detection on Resource-Constrained Devices
Ransomware remains one of the most destructive forms of cyberattacks, increasingly targeting mobile, IoT, and embedded systems with limited computational capabilities. Traditional deep learning–based ransomware detection solutions impose heavy computational overhead and are unsuitable for devices operating with constrained memory, battery, and processing power. This study proposes a lightweight machine learning framework designed to detect ransomware in real time using low-latency, low-complexity classifiers optimized for resource-constrained devices. Using the CIC-Ransomware 2020 dataset, which contains network-flow features of multiple ransomware families, six lightweight models Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machine (SVM-linear), Decision Tree (DT), and Random Forest (RF-light) were trained and evaluated. Feature reduction was performed using Mutual Information and Recursive Feature Elimination to limit the model to 12 optimal features suitable for edge deployment. Experimental results show that the optimized Random Forest and Logistic Regression models achieve high accuracy (97.8% and 94.6%), low inference time (<3 ms), and small memory footprints (<1.2 MB), demonstrating suitability for real-time ransomware detection on IoT gateways, smartphones, and microcontrollers. The results highlight that lightweight ML approaches when optimized can effectively secure edge devices without requiring heavy deep learning models.
Lightweight Machine Learning; Ransomware Detection; Network Traffic Analysis; IoT Security; Real-Time Detection; Edge Computing.
Analyzing Temporal Variations and Public Sentiment on X: A Clustering Analysis of the 2024 Pakistan Elections
X serve as vital arena for public discourse on current affairs, politics, and geopolitical events, as it encapsulate individual’s experiences and preferences, analysing public opinion within these digital spaces provides critical insights for shaping informed decision-making. This research examines public sentiments surrounding the 2024 Pakistan General Elections through the analysis of tweets collected within the due period. We employ a multi-faceted approach, integrating temporal analysis to pinpoint peak engagement periods, K-means clustering to identify distinct topical clusters, and VADER sentiment analysis to assess the prevailing emotions within each thematic group. The research explores the utility of common election-related hashtags to ensure the retrieval of an equally representative and non-influence opinion. Our findings reveal four prominent topics of discussion revolving around two main political parties, electoral processes, and public enthusiasm. Sentiment analysis indicated predominantly mixed sentiments towards the political parties. The integration of temporal patterns, topical clustering, and sentiment analysis exhibits practical and hands-on recommendations, offering political stakeholders and analysts a nuanced understanding of public sentiment dynamics, which can shape electoral strategies and decision-making processes.
Clustering, Machine learning, Sentiment analysis, Social media, User engagement
An Adaptive E-Assessment for Self-Learning: A Sustainable Education Model in the Post COVID-19 ERA of Digital Media
The integration of information and communication technologies in education has exploded various opportunities in learning and assessment. The state-of-the-art electronic learning and assessment systems are confined to delivering instructional content without focusing on learning analytics. Hence, more objective assessment systems are required that can keep track of the performance of students, especially for self-learning in the post-COVID-19 digital era. These assessment systems need to be optimized so that students can receive an accurate prescription in a limited time during the unavailability of teachers. Therefore, this study intends to propose an adaptive e-assessment model for learning and assessment. The proposed model comprises components including a domain model, student model, and assessment adaptation engine. The features of fuzzy logic have been utilized to address uncertainty and analyze student performance using a learner-centric approach. The prototype has been verified by deploying it on a computer science course offered at a degree-level program of an open university. The results reveal the improved performance of learners using the adaptive e-assessment system. The study also facilitates by providing a roadmap for the researchers to develop a generalized and personalized e-learning system for other courses.
Copyright (c) 2023 Journal of Information Communication Technologies and Robotic Applications
Adaptive Non-Crashing Functional Bugs in Android Applications
Because Android applications are so widely used, it is critical to make sure they are reliable and secure. Through completely automated functional fuzzing, this research presents a novel method for Android app security. Our approach uses sophisticated fuzzing techniques to methodically investigate the operation of Android apps, with a focus on the detection of non-crashing logic flaws. While non-crashing logic defects can introduce subtle vulnerabilities jeopardizing the overall integrity of Android apps, they are typically overlooked in traditional security testing, which concentrates on discovering and mitigating crashes. The current techniques for finding these logic issues that don't crash are frequently laborious, manual, and not very thorough. As of now, a general comprehension of functional defects remains elusive, impeding the progress of methodologies and strategies aimed at mitigating such issues. To address this deficiency, we investigate the fundamental causes, and symptoms of bugs, and test oracles, as well as the capabilities and limitations of current testing methodologies, using 401 functional defects from five prominent open-source and representative Android applications. This is the first systematic study of its kind. Several of the intriguing new findings and implications that our research uncovers cast light on the topic of addressing functional defects. By applying transfer learning and RegDroid to eight prominent real-world applications, we were able to effectively identify sixteen functional flaws.
Text-Based Sentiment Analysis Using CNN-GRU Deep Learning Model
Sentiment analysis identifies both positive and negative viewpoints from sources like social media, surveys, and reviews by automating text analysis with artificial intelligence (AI). Using data to inform decisions is made easier by this. Deep Learning (DL) has gained a lot of interest in recent years from academia and industry because of its outstanding performance. Convolutional neural networks (CNN) and recurrent neural networks (RNN) are the two deep learning designs that are most frequently utilized. Because they can examine enormous amounts of data, neural networks have the potential to be more accurate in sentiment analysis. Our work utilizes a hybrid model that combines Word2Vec preprocessing with the Gated Recurrent Unit (GRU) from Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) for sentiment analysis on movie and smartphone reviews. Achieving 99.99% precision, 99.84% recall, and 99.92% accuracy for movie reviews, and 99.08% accuracy, 98.93% precision, 99.40% recall, and 98.93% F1-score for Amazon mobile phone assessments. This paper presents a CNN-GRU-based Word2Vec algorithm for sentiment categorization, which addresses the challenge of evaluating vast amounts of user-generated text data with 99.50% accuracy.
Assessing the Usability of Real-Time Urdu Braille Applications: A Comprehensive Evaluation
Visual impairment encompasses both partial and complete loss of vision. Smart devices serve as efficient tools for assisting individuals with such impairments, empowering them to overcome their disabilities and improve their quality of life. Braille stands out as a widely adopted communication method among those with visual impairments. Touchscreen smart devices offer the ability to receive Braille input and promptly convert it into spoken language. However, many existing systems rely on input methods linked to specific locations, presenting challenges for visually impaired users. This study introduces a touchscreen-based Braille input method that remains accessible regardless of position, specifically designed for individuals with visual impairments. The aim is to minimize user effort by necessitating only taps on the relevant dots for each character. Through this interface, individuals with visual impairments can input Grade 1 and contracted Grade 2 Urdu characters by tapping anywhere on the screen. Evaluation metrics such as efficiency, accuracy, error rate, and user satisfaction were employed to assess the user experience with the proposed interface.
visually impaired; smart devices; urdu braille; mobile applications; usability; accessibility research;
A Novel Morphological Rule-based Approach for Urdu Text Sentiment Analysis
Sentiment Analysis (SA) is an exciting research area in Urdu text classification tasks. Due to its complicated morphology, sentiment analysis in Urdu poses various challenges. The lexicon-based technique is the preferred approach for Urdu sentiment analysis for its high accuracy rate. This approach requires a comprehensive manual list of positive and negative words. Sentiment classification can be effectively achieved using morphology-driven methods. For this purpose, an effective Urdu sentiment analyser employing morphological rules for performing Urdu sentiment analysis is proposed. The application of the rules depends on the input words' lexical category and prefixes. Sentiment classification rules are proposed based on the prefixes attached to negative sentiment carrier words. A thorough evaluation with reasonable Urdu text document/dataset size is performed. The empirical assessment proved that the proposed rule-based method achieved satisfactory precision, recall, and accuracy.
Natural Language Processing; Rule-based, Sentiment Analysis; Urdu Language
Rice Disease Classification using Deep Learning
Rice is one of the essential foods for most of the world’s population, and its cultivation is done in large areas of Pakistan. It satisfies the food demand and also plays a vital role in the economy of Pakistan. But rice leaf diseases can affect its production. Rice leaf diseases can be categorized into various diseases like brown spots, bacterial blight, tungro, and leaf blasts. Early detection of rice leaf diseases is critical for the effective management and prevention of crop losses. Effective treatment of these diseases can be done after their detection. Detection of such diseases can be performed using a deep learning approach. The small dataset allows transfer learning models and enhanced Xception models used to detect diseases effectively. The Xception model has achieved 93.92% test accuracy when the epoch value is set to 10. Model evaluation has also been done using accuracy, recall, precision, and confusion matrix. Deep learning approaches for the detection of rice leaf diseases in Pakistan can have a positive impact on rice production and the country's economy.
Copyright (c) 2022 Journal of Information Communication Technologies and Robotic Applications
Potato plant leaf disease classification using an Enhanced Deep Learning Model
Potato is one of the most important vegetable crops cultivated worldwide, but its production is often threatened by various leaf diseases, particularly early and late blight caused by Alternaria solani and Phytophthora infestans. These diseases can significantly reduce crop yield and quality, and farmers often rely on visually detecting changes in potato leaf color, which can be time-consuming and unreliable. To address this problem, there is a need for computer-aided techniques that can accurately and quickly identify these diseases, even in their early stages. In this paper, we propose a deep learning approach called Enhanced EfficientNET, which uses the Enhanced EfficientNET network to recognize various types of potato leaf disorders. To enhance the model's recognition ability, we introduce a spatial-channel attention method that focuses on the damaged areas. We also address the issue of class-imbalanced samples and improve the model's generalization ability by tuning the EANet model using transfer learning and adding dense layers to enhance its feature selection power. We test the model on a challenging dataset called PlantVillage, which contains images taken in diverse and complicated background conditions and achieve an accuracy of 98.12% for classifying various potato plant leaf diseases. Our experiments demonstrate the effectiveness of our approach in robustly tackling distorted samples and classifying potato plant leaf diseases.
Spatial-channel attention, Transfer learning.
IoT Environment Security and Privacy for Smart Homes
A network of connected things that can communicate and exchange data is known as the Internet of Things, or IoT. The number of these connected devices will be 38.6 billion in 2025 and devices are collecting data continuously from your location, contacts, calendar events, smart homes, health devices, etc. A number of security and privacy challenges arise due to its heterogeneity and use. The identification of the challenges and issues is important for better security and privacy. The research will focus on the current issues and challenges with an available solution for a smart home context. An IoT Smart Gateway concept has been introduced for dealing with the most common issues and challenges by using a pre-train machine learning model at the smart gateway, which deals with consumers and resource policies separately
IoT; Smart Home; IoT Security and Privacy
Machine Learning Techniques for Early Classification of COVID-19 Disease in Patients
Coronavirus (COVID-19) disease has been ravaging countries of the world for more than two years now. The disease is caused by the SARS-CoV-2 virus. It has been reported that when people are infected with the virus they may experience symptoms like mild or moderate respiratory sickness and they will be able to recover without requiring special treatment. Machine learning techniques have been identified to be very promising for establishing COVID-19 evidence in patients. Some of the past studies focused on the use of clinical images for COVID-19 disease classification. Thus, this work used identified medical symptoms in the chosen dataset for the classification purposes. The study specifically seeks to investigate the performances of two ensemble models when the dataset is pre-processed and selected promising features are used to train the models. Exploratory analysis was first of all carried out on the dataset with a view to understanding the patterns better. Then, a categorical variable in the dataset was encoded and subset features were selected with the aid of feature importance method. Thereafter, Random forest and AdaBoost algorithms were used to build coronavirus classification models from the dataset. The results showed that the two ensemble models performed better when a filter-based feature selection technique was used on the dataset compared to when all the features were used for building the COVID-19 classification models. For instance, the RF-based model record an accuracy of 0.89 and 0.96 without and with filter based feature selection respectively. Similarly, the Adaboost-based model recorded an accuracy of 0.90 and 0.97 without and with feature sub-set selection, respectively.
Medical diagnosis, Machine Learning techniques, Disease Classification, Detection Accuracy
Copyright (c) 2022 Aremu TB, Akinyemi Moruff OYELAKIN, Akanbi MB, Sulaimon HF, Odeyale KM https://creativecommons.org/licenses/by-nc/4.0
A Novel-Based Deep Learning Approach for Early Diagnosis of Cotton Crop Disease
Pakistan is among those countries where most of the GDP of a country depends on agriculture. As agriculture plays a vital role in the economic stability of Pakistan. The farmers are encountering various challenges in diagnosing diseases in cotton crops for the last decade. The attack of disease ultimately lessens the cotton yield and as a result, the economy of the country and the income of the farmers also go down. The detection of disease in crops at an early stage is crucial which can ultimately save farmers from loss. Manual detection is difficult and time-consuming an automated system to detect disease in plants with high accuracy will be handy for farmers. A deep learning approach is used in the proposed work to detect the disease in the crop plant. The Deep Convolutional Neural Networks model has been utilized to detect cotton plant disease using the image dataset of the cotton plant leaves. The model achieved a training accuracy of 99.08% validation accuracy of 100%. The proposed methodology will allow farmers to correctly detect diseases in the early stage. The diagnosis of plant disease helps to cure it in time. If the disease is controlled in time, its ultimate effect will be on the yield of the cotton crop. This will improve farmer income and the GDP of a country.
Bi-LSTM Deep Learning Approach for Employee Churn Prediction
Employee churn prediction is also known as ‘attrition’ or ‘turnover’ is referred to as the identification of employees planning to quit the organization in the future. Organizations invest time, effort, and money in employees’ training. Therefore, an experienced employee is an asset to the organization. If organizations could predict employee churn using machine learning techniques and can take timely measures, then they can prevent long-term loss. A number of machine learning models have been used for churn prediction of employees, such as Logistic Regression, Support Vector Machine, and MLP (Multi-Layer Perceptron). The aim of this study is to find the optimal algorithm of classification for the prediction of the churn employee rate. A deep learning approach based on B-LSTM (Bi-Directional Long Short-Term Memory) is being proposed and tested. The accuracy of B-LSTM is 97.5% during the consistency test. A comparative analysis with other machine-learning techniques is also performed and it is concluded that B-LSTM has proved more effective than other machine learning techniques investigated in this study.
Churn Prediction, MLP (Multi-Layer Perceptron), Bidirectional LSTM (B- LSTM), Gradient Boosting, Naïve Bayes, Organization
Copyright (c) 2021 Journal of Information Communication Technologies and Robotic Applications
Predictive Text Summarization: Improving Efficiency and Effectiveness
Automatic text summarization is essential for knowledge extraction and text classification. It provides the solution to the issue of data and information overloading. The volume of text data accessible has risen significantly in recent years from several sources. A large amount of text contains a variety of detail and insights that must be effectively documented. Text Summarization is the act of shrinking a text while maintaining the information values and transforming them into concise summaries that state the document's key objective. The proposed model extraction-driven text summarization entails extracting high-ranking sentences from a document based on term and phrase attributes and combining them to provide a description. The Fuzzy inference engine is used to model the document's summary and calculated by the relative value of the sentences in the document. The semantic approach to utilizing Latent Semantic Processing is explored, and the Fuzzy logic Extraction approach for text summarization. The proposed system suggested system has an average recall of 44.51, an average precision of 90.83, and an f-measure of 67.66. Our proposed summarizer's overall enhanced recall, precision, and f-measure efficiency than the fuzzy-based summarizer.
Yield Estimation of Citrus Fruit Using Color Based Segmentation and Circular Hough Transformation
Yield estimation using image processing techniques is an emerging domain of research. The yield estimation for citrus fruit is very important before harvesting fruit. The purpose of this study is to estimate the yield of citrus fruit using image processing techniques. Different researchers have developed different methods for yield estimation by using Image processing, Machine learning, and deep learning to estimate the yield of citrus fruits on time and to help the farmer to make timely decisions. A lot of work has already been done, but the accuracy in segmentation of fruits, occlusion, and different lighting conditions are still challenging problems to be addressed. The proposed technique eliminates these problems and improves overall detection accuracy. The proposed technique is based on two steps (i) color base segmentation, to segment the region of interest (Oranges), and (ii) circular Hough transformation for detection and counting of oranges. The overall accuracy of the system is 87% and the coefficient of determination R2 is 0.99 which shows the efficiency of the proposed approach
Yield estimation, Fruit detection, and counting, Color base segmentation, Circular hough transform.
COVID-19 Genomic Sequences Analysis using Google Variant Transformation
Coronavirus genomics has been of various patterns since the pandemic began. The variants appear to differ in terms of both transmission and infection rates consistently. It analyzes genomic variants of coronaviruses to extract insights for research. This work employed the Variant Transform tool to process the Coronavirus variant caller format files with big-query for genomic variant analysis enveloped on the Google Cloud Platform. We opted for Google Cloud Platform (GCP), particularly Google Cloud Life Sciences Suite with Free-tier Track. We downloaded genome sequences of COVID–19 and then transformed them into raw variant caller format files (VCF). Then analyzing COVID–19 VCF files by Google Variant Transforms tool. We have converted 30 coronavirus genomic sequences into Variant Caller format files using bioinformatics tools, then attained a table of coronavirus variant sites as variant residues through a big query and displayed the results with Data studio. Another part is Deep Variant's genetic analysis of non-viral genomic data, and its corresponding supported four model implementation. Our main breakthrough was storing Coronavirus Variant caller format files into big-query for Coronavirus Genomic analysis.
Detecting social bots activity in Twitter using deep learning approach
With millions of users, One of the most popular microblogging social networking sites is Twitter. Social bots (also known as spambots) are content-generating robots that exist on social media networks. This study provides an overview of new strategies for distinguishing between social bot accounts and human accounts that have recently appeared. Users have benefited from the deployment of social bots to automate analytical services and improve service quality. On the other hand, social bots have been employed to distribute false information, which can have real-world consequences. The describe a method for determining if that user is a Bot or a human. The propose recognizing such accounts by evaluating message predictability because communications published from the same bot account usually follow basic, repeating patterns. The shallow and deep learning algorithms for tweet-based bot detection, as well as their performance outcomes, are also described. To determine if tweets were made by actual people or bots, a machine learning architecture is proposed. Customers actual voices can be greatly distorted by malicious bots. We categorized millions of open-source functions in the cresci 2017 datasets. The goal of this study is to look at social bots in the context of user-generated content deep learning.
Automated Vulnerability detection for software using NLP techniques
Vulnerabilities can have devastating effects for information security, with farreaching ramifications for the economy, social security, and even national security. One of the most anticipated researches in the field of vulnerability detection is the use of some of the most cutting-edge NLP technologies to create models and accomplish the automatic analysis and detection of source code for particular text files such as source code. The proposed a deep learning-based vulnerability detection algorithm that extracts features using an RNN composite neural network. The employ a SARD and NVD dataset of lots of open source roles, which will be tagged with results after three static analyzers that hint to possible activities. By this dataset, develop a fast and accessible vulnerability detection system built on deep feature sign knowledge that directly translates source code. Our findings suggest that deep feature representation learning on source-code could be used to automatically uncover software vulnerabilities. In this study, the model software vulnerability detection as a natural language processing (NLP) problem with source code handled as texts, and use advanced deep learning NLP models with transfer learning on written English to address automated software vulnerability detection. Different algorithms will be compared (machine learning and deep learning).