International Journal of Innovative Technology and Interdisciplinary Sciences

Mapping ISP Malware Trends in Albania: Clustering for Smarter Cyber Defences

Klorenta Pashaj, Eralda Gjika — Thu, 04 Sep 2025 00:00:00 +0200

Cybersecurity plays a vital role in protecting digital infrastructure, with Internet Service Providers (ISPs) standing at the core of this ecosystem. This research takes an exploratory perspective, given the limitations of both the dataset and the number of available features. The analysis draws on malware detection data from Albania, reported through the Shadow Server platform, covering a 15-month period across seven ISPs. By applying time-series clustering alongside statistical methods, the study groups ISPs according to their security patterns. The time-series analysis points to three distinct periods of heightened malware activity, while the characteristics-based approach identifies three groups of ISPs that differ in their vulnerability profiles. Taken together, these results underline the need for customized cybersecurity strategies and stronger cooperation among ISPs. Despite the constraints of a relatively small dataset, clustering techniques prove useful for optimizing resources, supporting regulatory compliance, and informing strategic decisions aimed at more effective threat prevention and mitigation.

Fusion of Blockchain, IoT, Artificial Intelligence, and Robotics for Efficient Waste Management in Smart Cities

Guma Ali, Denis Asiku, Maad M. Mijwil, Ioannis Adamopoulos, Marek Dudek — Mon, 08 Sep 2025 00:00:00 +0200

Rapid urbanization and population growth are accelerating waste generation in cities worldwide, posing serious environmental and socio-economic challenges. Traditional waste management systems, often centralized and infrastructure-deficient, struggle with inefficiencies, unscheduled collection, and a lack of real-time data. These limitations hinder progress toward smart and sustainable urban environments. Blockchain, the Internet of Things (IoT), Artificial Intelligence (AI), and Robotics are reshaping waste collection, sorting, and recycling. This review examines how these technologies integrate to create secure, efficient, and sustainable waste management in smart cities. An analysis of 184 studies published between January 2022 and July 2025 reveals key shortcomings in conventional waste management systems and showcases the benefits of smart waste management solutions. The results showed that cities are already using IoT-enabled smart bins, AI-driven route optimization, Blockchain for waste tracking, and robotic sorting. However, challenges such as data privacy concerns, limited Blockchain scalability, system interoperability gaps, sensor reliability issues, and high computational demands limit broader adoption. The review outlines future research priorities, including AI-powered waste forecasting, swarm robotics, real-time edge computing, and enhanced cybersecurity. By providing a roadmap for technological innovation and integration, this study supports policymakers, urban planners, and industry leaders in developing intelligent, cost-effective, and environmentally resilient waste management systems.

SCP-IoT: Enhancing IoT Communication Security Against Routing Attacks

Tue, 09 Sep 2025 00:00:00 +0200

The Internet of Things (IoT) needs to be protected while in transmission. Insecure Internet of Things equipment connectivity can direct to security breaches. As a result, third parties can get access and make changes in order to cause problems for things connected in the system. In order to address these difficulties, the IoT communication security needs to be addressed. A new strategy named "secure communication utilising cryptographic approaches for IoT" was presented in this research to deal with this problem. There are three parts to the model, which is called the "safe communication protocol for IoT." First, the initiator sends a connection request to the respondent with the source identification and a true cryptography nonce to initiate the communication. Secondly, the responder examines the nonce's freshness and the source's identity when it receives a request. After that, the responder uses KDH to compute and deliver the MAC result for the SRC ID as part of the Finish message to the initiator. Few current strategies, including developing constrain fuzzy routing principles, were evaluated and compared to the proposed model. Prior to this study, the most important metrics were the MLR and MDR ratios, the spectrum utilisation rate, the network lifetime, and the utilisation rate.

Developing a Conceptual Framework for Soil Property Analysis and Crop Yield Prediction Using Machine Learning Techniques

Vimla Dangi, Chandrashekhar Goswami, Prasun Chakrabarti — Fri, 12 Sep 2025 00:00:00 +0200

The most important single factor is soil fertility which influence crop sustainability and agricultural productivity. The necessity to use data-driven approaches to assess the health of the soil and propose the crops that should be grown in it has become a crucial issue because the accuracy of agriculture is required increasingly frequently. Based on the dataset of the Soil Health Card (SHC) of the Government of India, the presented study provides a conceptual framework that involves the application of the machine learning approaches to analyse soil characteristics and predict its agricultural productivity. The framework is based on twelve important soil parameters: sulphur (S), nitrogen (N), zinc (Zn), phosphorus (P), electrical conductivity (EC), potassium (K), manganese (Mn), copper (Cu), boron (B), iron (Fe), organic carbon (OC), and pH to cluster soil samples into the categories of low, medium, and high soil fertility by using the K-means algorithm. To suggest the correct crops that must be grown in each of the fertility categories, the Random Forest Classifier is then trained after the clustering. The model is checked by K-Fold cross-validation (k=5) and Holdout (80/20 split) to make sure that in unseen data strong generalization will be achieved. An average performance of 91 percent in K-Fold, and zero in holdout validation showing no inaccuracies in dividing the test set and an RMSE and MAE also zero, results indicate high performance and no mistakes in classification. Also, the proposed methodology enhances the agronomic decision-making with the help of AI-based crop proposals targeting each of the fertility classes. This study is an indication of the efficiency of the integration of supervised and unsupervised methods in agricultural informatics. It attracts interest in how intelligent models can high-grade the use of resources, encourage sustainable agriculture and endow growers with useful information based on real-life DO data.

Energy Generation from Water Systems: A Technical and Cost-Benefit Analysis

Gjelosh Vataj, Meshdi Ismayilov, Zenel Sejfijaj — Mon, 15 Sep 2025 00:00:00 +0200

The production of energy from the flow of water in drinking water supply pipes is an emerging field globally, and particularly novel in Kosovo. This method involves integrating water turbines with generators directly into water pipes, utilizing the water flow to rotate blades and drive the rotor, thereby producing electricity. Such systems not only generate renewable energy but also reduce excess pressure within the pipeline network, providing a dual benefit. Although similar technologies exist worldwide, their practical application in potable water systems remains limited, with experts yet to fully embrace their potential for reliable power generation. This paper explores the feasibility of implementing such a system in the Regional Water Company “PRISHTINA,” with the aim of using the generated electricity to power monitoring equipment in the water supply network. The proposed approach has the potential to enhance operational efficiency, generate additional revenue, and mitigate risks associated with high pipeline pressure. This paper provides novel insights into the technical, financial, and environmental benefits of harnessing energy from existing water distribution systems in underdeveloped regions.

Strategic Human Resource Management and Its Impact on Organizational Performance: Empirical Insights

Klara Prifti, Boriana Vrusho, Çlirim Toci, Llambi Prendi, Jonida Bushi Gjuzi — Thu, 18 Sep 2025 00:00:00 +0200

In light of today’s dynamic economy, Human Resource Management (HRM) has become a strategic lever for enhancing organizational performance, resilience, and long-term competitiveness. This study presents an original pioneering investigation into the strategic role of HRM within the Albanian private sector, making the first study of its kind in the country. It introduces an innovative conceptual model that integrates both HR roles and practices as dual expressions of HR strategy, offering a novel and internationally relevant framework. Grounded in more than two decades of international literature, the study applies a robust econometric approach to examine the multidimensional relationships between HRM and the performance of the organization. Data were collected through a structured questionnaire among private companies of varying sizes in Albania with over fifty employees. The evaluation was conducted using the Structural Equation Model (SEM) via the "LaVan" package in R. Findings reveal that key strategic components business strategy, the role of Human Resources, HR practices, and HR outcomes collectively contribute to strategic and internal fit, significantly enhancing organizational performance. By addressing a critical research gap in developing economies, this study establishes a strong theoretical and methodological foundation for future research both in Albania and in other transitional markets. The implications are particularly relevant for scholars, policymakers, and business leaders seeking to advance sustainable performance through strategic human resource development.

A Latent Dirichlet Allocation Framework to Analyse and Forecast Employability Skills

Milena Shehu, Areti Stringa, Eralda Gjika Dhamo — Sat, 20 Sep 2025 00:00:00 +0200

Recently globalization, technological advancement, reorganization of many job positions, have substantially influenced and profoundly transformed the nature and attributes of the labour market in both industrialized and emerging nations. Data pertaining to the online labour market has become a valuable resource for comprehending the dynamics and trends of the job market. Policymakers in Albania must benefit from machine learning algorithms to analyse and forecast skill needs. This paper presents a Latent Dirichlet Allocation (LDA) framework, as a probabilistic topic-modelling technique employed to find latent topics within a dataset of job vacancies. The analysis employs a two-step LDA model. The initial step is the Latent Dirichlet Allocation Model, which detects latent semantic components. The second step of the model maps semantic components calculated by the LDA algorithm to map job vacancies from the topics identified from LDA model into each category of skills. A Shiny app has been developed to forecast the top 10 skills and interests needed, categorized by skill type. The methodology and findings of this study, of this paper are expected to support Albania’s public employment institutions to perform a screening of the current soft skills and interests and predict future skills required by companies operating in Albania.

Assessment of Seasonal Fluctuations in Heavy Metal and Bacterial Pollution in the Euphrates River near Najaf, Iraq

Karrar Abbas Zwain, Mohammed Jawad Al-Haidarey — Sun, 21 Sep 2025 00:00:00 +0200

This research work assessed seasonal variations in physicochemical parameters, heavy metals, and bacterial contamination in the Euphrates River near Najaf, Iraq, from December 2023 to November 2024. Results revealed marked seasonal fluctuations in water temperature, ranging from 14.80 ± 2.04 °C in winter to 30.31 ± 1.01 °C in summer. Total dissolved solids (TDS) were highest in winter (924.19 ± 44.26 mg/L) and lowest in summer (652.74 ± 37.50 mg/L). While pH, dissolved oxygen (DO), and biochemical oxygen demand (BOD5) remained within international standards, TDS exceeded the World Health Organization (WHO) aesthetic guideline, and concentrations of lead and cadmium surpassed both WHO and U.S. Environmental Protection Agency (USEPA) limits. Lead concentrations increased substantially from spring (0.05 ± 0.02 mg/L) to autumn (1.47 ± 0.31 mg/L). Total coliform bacteria (TCB), indicative of faecal contamination, were present in all samples. Correlation analyses suggested that industrial effluents and untreated sewage represent common sources of heavy metals and bacterial pollutants. The findings indicate that the Euphrates River water in this region is unsuitable for direct consumption without advanced treatment and presents significant risks to human health and the aquatic ecosystem.

Machine Learning for Legal Compliance in the Energy Sector: A Predictive Regulatory Framework

Tue, 23 Sep 2025 00:00:00 +0200

Increased complexity in energy regulations and sustainability standards has created a pressing need for automated regulatory compliance monitoring systems. A predictive regulatory system integrating legal compliance analysis with machine learning techniques in the energy sector is proposed in this work. On the Energy Efficiency dataset, Linear Regression, Support Vector Machines (SVM), and Random Forest were used to predict building energy loads and determine compliance with regulatory standards. The research demonstrates that machine learning enhances not just the precision of forecasts but also proactive identification of non-compliant cases, reducing legal vulnerabilities and helping policymakers implement standards of efficiency. Statistical measures and correlation determine the most impactful features, and relative performance metrics (accuracy, precision, F1, and R²) determine the robustness of the models. The system bridges the gap between energy engineering and regulation law and provides an energy sector compliance management solution that is scalable and data-driven.

Hybrid Grey Wolf and Genetic Algorithm for the Flow Shop Scheduling Problem

Thu, 25 Sep 2025 00:00:00 +0200

The Flow Shop Scheduling Problem (FSSP), a pivotal NP-hard combinatorial optimization challenge, is central to enhancing manufacturing efficiency by minimizing makespan across n jobs and m machines. This study introduces a novel hybrid metaheuristic that integrates Grey Wolf Optimization (GWO) for robust global exploration with Genetic Algorithm (GA) for precise local exploitation, augmented by adaptive crossover, mutation, and 2-opt local search, addressing a significant gap in synthesizing swarm intelligence and evolutionary techniques for permutation-based scheduling. Evaluated on 13 Taillard benchmark instances (20-200 jobs, 5-20 machines) over 50 runs, the GWO-GA algorithm demonstrates superior performance compared to established metaheuristics, including SGA, HMSA, NEH, DDE-PFS, DSADE-PFS, and DSADEPFS, with statistical validation via ANOVA and Tukey HSD tests. The study highlights the algorithm's robust convergence and scalability, marking a key contribution to scheduling optimization. Its ability to outperform existing methods underscores its practical significance, while computational overhead for large instances suggests future exploration of parallelization and multi-objective enhancements.

A Modified PRoPHET Protocol for Energy and Buffer Optimization in Delay Tolerant Networks: Performance Evaluation for an IoT Smart City Scenario

Evjola Spaho, Orjola Jaupi, Kristjan Toplana, Andeta Ilnica — Mon, 06 Oct 2025 00:00:00 +0200

In smart cities, Delay Tolerant Networks (DTNs) support Internet of Things (IoT)-based services, where the density of interconnected devices is very high, making energy management even more critical. Considering energy-constrained scenarios, optimization of energy consumption will ensure the longevity of the individual nodes and sustainability of the whole infrastructure of the smart city. In such scenarios, energy-aware routing is a very important solution for efficient management of limited energy resources. In this work we present a modification of the Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET) protocol that integrates both energy-aware message forwarding and Acknowledgment (ACK) based buffer management to enhance the efficiency of message delivery in DTNs, especially in smart city IoT scenarios with limited energy and buffer resources. To validate the effectiveness of this modification, simulations are conducted to compare the performance of the modified PRoPHET protocol with the original version. The key metrics for evaluation include delivery probability, routing overhead and average buffer time. The modifications improve performance under energy constraints while managing buffer utilization more effectively.

Machine Learning-Driven Cross-Layer IDS Architecture for Next-Generation IoT Networks

Yuva Krishna Aluri, Saravanan Tamilselvan — Sun, 12 Oct 2025 00:00:00 +0200

The proliferation of Internet of Things (IoT) devices across critical infrastructures introduces significant security risks due to their heterogeneous and resource-constrained nature. This study extends cross-layer Intrusion Detection System (IDS) research by systematically comparing three machine learning models—Support Vector Machine (SVM), Random Forest (RF), and a hybrid CNN-LSTM—using benchmark datasets (NSL-KDD, BoT-IoT, and CICIDS2017). Unlike prior works that focus on single-layer IDS or isolated model evaluation, our approach aggregates features from multiple OSI layers (network, transport, and application), providing a holistic view of IoT traffic. The findings demonstrate that CNN-LSTM achieves the highest detection accuracy (97.4%) but requires substantial computational resources, whereas RF offers a near-optimal trade-off between accuracy (96.8%) and efficiency, making it suitable for deployment on resource-constrained IoT devices. Scalability analysis confirms stable detection performance up to 200 IoT nodes with only minor accuracy degradation. This work highlights both the strengths and limitations of cross-layer ML-based IDS and provides insights for future enhancements through lightweight deep learning, federated learning, and explainable AI (XAI) for 6G-IoT environments.