International Journal of Innovations in Science & Technology
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Irrational Beliefs and Psychological Violence Among Adolescents
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Assessing the connection between teenagers\u27 psychological violence and illogical ideas was the aim of the research study. A proportionate stratified sampling technique was used to collect data from selected colleges of the Gujrat District. A sample of 1000 adolescents with an age range of 15-20 was approached for data collection. Irrational beliefs were measured with the help of 4 indicators (demandingness, awfulizing, limited frustration tolerance and global evaluation/ self-downing) and psychological abuse was measured with the help of seven indicators (verbal abuse, control and coercion, isolation, gas lighting, emotional manipulation, blaming and scapegoat and degradation and humiliation) on a 5-point Likert scale. The data analysis included descriptive statistics, correlation analysis, and multivariate regression analysis. Results indicate all factors were positively correlated with each other
Deep Learning Based Medicinal Plant Identification for Enhanced Botanical Conservation
Full text linkPlants used in medicine are an essential part of the human health system with various natural medicines and health properties. The right identification of medicinal plants will support the conservation of these natural resources and enhancement of these traditional medical practices. Medicinal plants can now be identified and classified more precisely and reliably by use of leaf and plant pictures using the technology of artificial intelligence and machine learning, especially deep learning. We used Convolutional Neural Networks (CNNs) deep learning models with transfer learning VGG11, ResNet34, and DenseNet121. The novelty of our study is that we combine DenseNet121 with the Multi-Trend Binary Code (MTBC) feature descriptor to perform better and extend features representation. These models have been tested on two benchmark datasets, which include the Indonesian Medicinal Plants Dataset as well as the Indonesian Herb Leaf Dataset. Although all CNN models performed well in terms of accuracy, the proposed hybrid model, DenseNet121+MTBC, performed better than the remaining, attaining its best accuracy of 94.51%, and offering better precision, recall, and F1-score metrics. The results note the usefulness of the combination of the traditional texture descriptors and deep learning features, thus, the synergistic trait of the hybrid approach. The hand-crafted features combined with DenseNet121 give a more effective solution to the repetitive phenomenon of medicinal plant identification than just any CNN. The method offers a convenient and efficient method of alternative relying on conventional methods of identification, offering proficient, exact, and advantageous rapid medication identification of plants
Application of Geospatial Approaches for Evaluation of Urban Growth Pattern and Trend Prediction of Multan City, Pakistan
Full text linkThis research purposes to evaluate the changes in land use, land cover (LULC) in the study area and scrutinize the urban growth trends in Multan City over a period of 30 years, from 1993 to 2023. Moreover, the research utilizes an Artificial Neural Network (ANN) model to implement urban expansion up to the year 2050. To achieve these goals, geospatial systems and approaches are applied. Satellite imagery and remote sensing data from the years 1993, 2003, 2013, and 2023 are analyzed to detect LULC changes. The classification of these images provides valuable insights into the transformation of Multan’s urban landscape over time. A supervised classification technique is primarily utilized to identify specific land cover classes. Landsat 5 data is used for the years 1993 and 2003, Landsat 7 for 2003, Landsat 8 for more recent observations, and Landsat 9 for the latest satellite imagery. The core geospatial model applied in this study is the Cellular Automata–Artificial Neural Network (CA–ANN) model, which is used to simulate and quantify urban expansion. Based on the CA–ANN model results, the urban area in Multan was approximately 154.84 km² in 1993, which expanded to 587.21 km² by 2023. Projections indicate that this urban area will further increase to 992.64 km² by 2030 and could reach 3,184.59 km² by 2050. These findings highlight a significant and rapid urban expansion expected in the coming decades
Improving Communication Quality Through Anonymous Communication: An Experimental Study using ANONI Application
Full text linkQuality of communication is closely related to quality education, United Nations Sustainable Goal. The performance of students can be analyzed through effective communication between students with their instructors. Multiple reasons cause poor or ineffective communication from students, for instance, shyness, fear of being criticized, and nervousness. Communication in anonymous mode is explored by various research studies. It is noticeable from the literature that students’ participation is directly related to anonymous communicationincrease. Although anonymous communication has a positive effect on student participation, this anonymous factor also causes disruptions or unanticipated negative intrusions during class discussions. This study aims to improve the quality of anonymous communication and explore the impact of anonymous communication on students with less participation. The study\u27s objective has been achieved about undergraduates enrolled in software engineering programs. The reward-based synchronous & asynchronous web application named “ANONI” was utilized for this purpose. The results show a positive increase in participation and constructive communication of students during the session, as only 2 off-task activities were observed
Deep Learning Based Sentiment Analysis on Instagram Insights of Consumer Behavior for Improving Business Decision Making
Full text linkThe increasing use of social media platforms such as Instagram has made them a significant source of consumer insights for businesses, highlighting the importance of automated sentiment analysis. This study aims to address the challenge of accurately classifying consumer sentiments in Instagram posts, where informal language, slang, and sarcasm often reduce the effectiveness of traditional models. To overcome this gap, two deep learning approaches were employed: a Bidirectional Long Short-Term Memory (BiLSTM) network as a classical recurrent baseline and transformer-based architectures (BERT and DistilBERT) as state-of-the-art models. A dataset of 184,010 Instagram posts was preprocessed, tokenized, and mapped into positive and negative sentiments, and the models were trained and evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. The results demonstrated that BERT achieved the highest performance with an accuracy of 0.91 and an F1-score of 0.91, outperforming BiLSTM (accuracy 0.87, F1-score 0.86), while DistilBERT provided a competitive balance between accuracy (0.89) and efficiency. These findings confirm that transformer-based models, particularly BERT, are better suited for capturing nuanced sentiments in social media text. The study concludes that models can provide actionable insights into consumer behavior, enabling businesses to enhance brand monitoring and customer engagement
Optimization of Production Planning with Python’s SciPy: A Computational Study
Full text linkProduction planning optimization is the act of effectively distributing limited resources, including labor, materials, and equipment, to achieve production targets while optimizing profit and reducing waste. This study analyzes how optimization methods can be applied to production planning models in the cooking oil sector, with a particular emphasis on how linear programming (LP) can be used to handle usable quality limitations to maximize gross profit. The goal of this study is to find the best values for decision variables across a variety of inventory-based production frameworks. It is important in a manufacturing zone where input bound must be weighed against consumer needs, such as the industry of cooking oil. In order to provide a computational method for determining the perfect production levels, the study establishes a linear programming (LP) model and solves it using Python’s SciPy package. This optimization method uses objective functions involving dense matrices and numerical equations to solve the production planning problem. In calculating output levels and profit margins, the numerical results show a significant convergence, rating the effectiveness and credibility of the suggested approach in providing the optimal solution for practical industrial planning
Hydraulic Modelling of Flood Assessment in Chenab Basin Pakistan
Full text linkFlooding represents one of the most recurrent and economically damaging natural hazards in Pakistan, particularly within the Indus River system, where extensive floodplains and dense human settlement exacerbate vulnerability. This study presents an integrated flood-assessment framework combining optical remote sensing, geographic information systems (GIS), and physically based hydraulic modelling to delineate and quantify flood inundation in the Chenab Basin, Punjab. Multi-temporal Landsat 8 OLI imagery acquired during pre-, peak-, and post-flood stages was processed to derive the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Water Ratio Index (WRI) for flood detection. Comparative accuracy assessment using field observations and high-resolution reference imagery demonstrated that MNDWI outperformed other indices, achieving an overall accuracy of 91% compared to 83% for NDWI and 85% for WRI. Supervised maximum-likelihood land-use/land-cover (LULC) classification yielded an overall accuracy of 91.6% with a Kappa coefficient of 0.89, confirming strong agreement between classified outputs and ground reference data. A 30 m SRTM-derived Digital Terrain Model was employed to develop a one-dimensional hydraulic model in HEC-RAS, simulating flood scenarios for return periods ranging from 2.5 to 100 years (455,000–1,665,000 cusecs) along the Chenab River reach between Head Trimmu and Head Panjnad. Modelled water-surface elevations showed close correspondence with GPS-recorded flood marks, with positional deviations below 50 m and sensitivity analysis indicating a maximum ±0.15 m variation in water level for ±0.01 changes in Manning’s roughness coefficient. Results indicate that approximately 68% of the study area was inundated during the 2010 flood, with cropland accounting for nearly 61% of the affected area and settlements for 18%. The integration of satellite-derived water indices with hydraulic simulation proved effective for accurate flood delineation and hazard zoning, providing a robust and operationally scalable framework for flood-risk assessment and spatial planning in data-scarce river basins of Pakistan
Preliminary Medical Diagnosis Using Voice-Based Urdu Language Interface
Full text linkExpert knowledge is stored in the knowledge base through an externalizing process in the form of facts, procedures, heuristics, and rules. The knowledge base helps to refine the present knowledge and insert new knowledge without recompiling a program. Medical diagnosis is one of the first knowledge-based areas in which expert system principles are applied. Almost all knowledge-based medical diagnostic systems take input symptoms in the form of text and rely on the English language. This is a hindrance to illiterate and non-native English speakers of developing countries to utilize the system, and unfortunately, Pakistan is one of them. In this connection, this paper proposed an indexing method for integrating the medical diagnostic knowledge base with a Pakistani National Language-based voice-oriented user interface for accommodating the illiterate
Agentic AI for Autonomous Soil and Fertilization Management for Agriculture Sustainability
Full text linkSoil fertility loss and excessive chemical fertilization are major environmental and economic issues in developing regions such as Punjab, Pakistan. This paper proposes an Agentic AI framework for autonomous soil and fertilization management that combines (i) IoT soil sensing and drone-based crop monitoring for real-time perception, (ii) predictive modelling for short-horizon nutrient and moisture forecasting, and (iii) multi-agent reinforcement learning (MARL) for adaptive decision-making. The system operates with operational autonomy, executing daily management decisions without routine human-in-the-loop control. Agronomic expert knowledge is incorporated only offline as safety constraints and initialization priors (e.g., allowable nutrient ranges and stress-avoidance rules) to bound the action space and prevent unsafe behavior, rather than to prescribe actions. Experiments were conducted across two seasons at two sites (Sheikhupura and Multan) under four treatments: Farmer Practice (FP), Rule-Based Control (RBC), Machine Learning Predict (ML-Predict), and the proposed Agentic AI. Results show that Agentic AI reduces nitrogen fertilizer use while maintaining/improving yield proxy and improving soil indicators (including residual nitrate reduction and improved Soil Health Index). We also analyze irrigation outcomes as a sustainability objective and show how water usage must be treated as a constrained or multi-objective term in the reward function to avoid over-irrigation. Overall, the framework supports scalable, data-driven soil management with bounded autonomy, preserving expert-defined agronomic safety
Olive Leaf Disease Detection Using Transformer-Based Deep Learning Approach
Full text linkThe use of AI and DL in automated crop health monitoring and disease diagnosis, especially relevant to Pakistan\u27s burgeoning olive growing industry, has gained momentum. This paper proposes a transformer-based deep learning approach for the detection of olive leaf diseases due to significant shortcomings in the robustness and generalization of traditional convolutional neural networks. The proposed system makes use of a Vision Transformer (ViT) architecture to extract both local and global contextual features from the images of leaves using multi-head self-attention mechanisms. The developed Optimized ViT-Small model identifies olive leaves into three classes: Healthy, Aculus olearius, and Olive Peacock Spot. It is trained and tested on a pre-processed dataset of 3,400 high-resolution olive leaf images collected from olive-growing regions of Pakistan. Experimental results show strong performance with a test accuracy of 97% while demonstrating high precision, recall, and F1-scores throughout the classes. Moreover, performance assessment through confusion matrix analysis, ROC AUC, and precision-recall curves supports the developed model\u27s effectiveness. Although the dataset\u27s geographical coverage is limited, the results indicate that transformer-based architectures are an attractive alternative for the applications of precision agriculture in Pakistan